Real estate abstract
Systems and methods of the invention provide objective evaluations
of a business entity's real estate situation and condition for use
by customers including (but not limited to) the business entity.
Information is processed to determine indicators of amount, price,
area, grade, and risk; and those indicators are combined to provide
a total score. The system includes a database for storing a variety
of data, such as utilization measures and business information,
and data corresponding to businesses which are similar to the business
entity. Process actuators process the information to derive the
several indicators, the score, and other measures, which is printed
or displayed for customers and/or the business entity. Preferably,
a report is generated which details information including the score
to provide a well-rounded picture of a particular real estate situation.
Real estate claims
In view of the foregoing, what is claimed as new and secured by
the Letters Patent is:
1. A method for evaluating real estate for use by a Business Entity,
comprising the steps of:
storing utilization information about the real estate in a database,
the utilization information including (i) square footage data representing
a square footage of the real estate, (ii) data characterizing the
selected use of the real estate, (iii) cost data including a rental
price of the real estate, (iv) data about the number of the Business
Entity's employees in the real estate, and (v) data about the sales
or revenue of the Business Entity in the real estate,
storing data representing utilization values in the database from
business entities which are similar to the Business Entity,
determining a utilization indicator of the real estate by processing
information in the database, the utilization indicator having a
numerical representation and being a function of (i) the square
footage, (ii) the selected use, (iii) the cost data including the
rental price, (iv) the employee data, and (v) the sales data,
processing the numerical representation to produce a score representing
a quantitative evaluation of the real estate,
generating a report that includes the score, and
displaying the report for use by the Business Entity.
2. A method according to claim 1 wherein the real estate comprises
one of (i) a single location occupied by the Business Entity, and
(ii) multiple locations occupied by the Business Entity.
3. A method according to claim 1, further comprising the steps
of:
extracting from a business database space utilization data records
of other business entities having a standard industrial classification
code that is similar to the Business Entity's standard industrial
classification code, each of the space utilization data records
including information representing at least one of (i) a square
footage of real estate occupied by one of the other business entities,
(ii) a number of employees of one of the other business entities
in the real estate, and (iii) sales or revenue of the Business Entity
in the real estate,
determining space utilization values from the extracted space utilization
data records by calculating at least one of a (i) square footage
per employee of the Business Entity in the real estate; and (ii)
a sales or revenue per square foot of the Business Entity in the
real estate,
determining an average and standard deviation for the space utilization
values,
determining a variance between a space utilization value of the
Business Entity in the real estate and the average space utilization
values, and
assigning a value to the indicator based upon a comparison between
the variance and the standard deviation.
4. A method according to claim 1 wherein the step of determining
a utilization indicator comprised the step of determining the utilization
indicator on a periodic basis.
5. A method according to claim 1, further comprising the steps
of:
extracting from a business database cost utilization data records
of the other business entities which have one of (i) a standard
industrial classification code that is similar to the Business Entity's
standard industrial classification code, and (ii) a location with
a real estate submarket that is similar to a submarket of the Business
Entity, each of the cost utilization data records including information
representing at least one of (i) a number of employees occupying
real estate of the business entities, (ii) a square footage of the
real estate, (iii) rental costs of the real estate, and (iv) sales
or revenue of the business entities in the real estate,
determining cost utilization values from the extracted cost data
records by calculating at least one of a (i) rental price per square
foot of the real estate, (ii) rental price per employee of the real
estate, and (iii) sales or revenue of the other business entities
in the real estate per rental price of the real estate,
determining an average and standard deviation for the cost utilization
values,
determining a variance between a cost utilization value of the
business entity and the average cost values of the similar business
entities, and
assigning a value to the indicator based upon a comparison between
the variance and the standard deviation.
6. A method according to claim 1, further comprising the steps
of (i) determining a grade indicator of the real estate, the grade
indicator having a numerical representation and being a function
of (i) a classification system of the real estate that is consistent
with accepted standards, and (ii) application of that system to
buildings occupied by the business entities which are similar to
the Business Entity, the grade indicator thereby providing a numerical
representation of the quality of the real estate, and (ii) processing
the grade indicator with the numerical representation of the utilization
indicator to produce the score.
7. A method according to claim 6 wherein the classification system
comprises a numerical representation of the property grades.
8. A method according to claim 6 wherein the step of determining
a grade indicator comprises the further steps of (i) extracting
from a data source, location data records of the business entities
which have a standard industrial classification code that is similar
to the Business Entity's standard industrial classification code,
and (ii) storing the location data records on the database.
9. A method according to claim 6, further comprising the step of
assigning a classification to the buildings based upon at least
one of the following: (i) a building grade as reported by experts
familiar with classification systems and generally accepted standards,
(ii) an age of the building, and/or (iii) an extrapolation of comparable
buildings.
10. A method according to claim 6 wherein the step of determining
a grade indicator comprises the further step of assigning grade
classifications to the buildings.
11. A method according to claim 10 wherein the step assigning grade
classifications comprises the further step of assigning a range
of numerical values to the buildings based on the assessed grade
of the buildings.
12. A method according to claim 6, further comprising the steps
of determining an average and standard deviation for the grade classifications,
determining a variance between the grade classification of the
Business Entity's real estate and the average grade classifications
of the real estate of the similar entities, and
assigning a value to the grade indicator based upon a comparison
between the variance and the standard deviation.
13. A method according to claim 1, further comprising the steps
of (A) determining an area indicator of the real estate, the area
indicator having a numerical representation and being a function
of at least one of (i) rents, (ii) vacancy, and (iii) absorption
rates for each of the Business Entity's submarket and nearby submarkets,
and (iv) other indicators of submarket and location attractiveness,
the area indicator thereby being a measure of the attractiveness
of the real estate, and (B) processing the area indicator with the
utilization indicator to produce the score.
14. A method according to claim 13 wherein the step of determining
an area indicator comprises the further steps of defining a list
of submarkets, extracting from a real estate market database, for
each of the submarkets, at least one of the following submarket
values: (i) rents, (ii) a vacancy rate, and (iii) an annual absorption
rate;
calculating submarket rankings for the submarkets based upon the
submarket's values compared with other submarkets within the same
market, and
developing a submarket ranking for the location of the real estate.
15. A method according to claim 13, further comprising the step
of assigning a value to the area indicator based upon a comparison
between the Business Entity's submarket and nearby submarkets.
16. A method according to claim 1, further comprising the step
of determining a risk indicator of the real estate, the risk indicator
having a numerical representation of a financial, market, and environmental
exposure of the real estate, the risk indicator being a function
of at least one of (i) a building age, (ii) a presence of asbestos,
(iii) a presence of toxic waste sites and/or other man-made environmental
hazards, (iv) a presence of a naturally occurring environmental
hazard, (v) a presence of financial obligations and encumberances
of the Business Entity for the real estate, (vi) a tenancy status
and/or remaining time before a financial obligation of the Business
Entity for the real estate, is terminated; and/or other measures
of financial, market and environmental risks, as defined by industry,
professional and/or governmental organizations and generally accepted
in industry or professional practice.
17. A method according to claim 16, further comprising the step
of evaluating at least one of the following: (i) an age of the real
estate, (ii) a risk of exposure to asbestos in the real estate (iii)
a proximity of one or more toxic waste sites to the real estate,
(iv) a proximity of one or more naturally occurring environmental
hazards to the real estate, (v) any remaining financial obligations
and encumberances of the Business Entity for the real estate, (vi)
any tenancy status of the Business Entity for the real estate, (vii)
other measures that indicate potential health and safety risks for
occupants of the real estate, and financial and market risks of
the Business Entity for the real estate.
18. Apparatus for evaluating selected real estate for a selected
use by a Business Entity, comprising:
a database for storing information about the real estate and about
comparable real estate properties of business entities which are
similar to the Business Entity, the database comprising
means for storing space utilization information, including (i)
square footage data representing a square footage of the real estate,
(ii) data characterizing the selected use, (iii) data about the
number of employees in the real estate, and (iv) data about the
sales or revenues of the Business Entity in the real estate,
means for storing cost utilization information, including (i) the
square footage data, (ii) the data characterizing the selected use,
(iii) rental data representing a rental price of the real estate,
(iv) data about the number of employees in the real estate, and
(v) data about the sales or revenues of the Business Entity in the
real estate,
means for storing comparable real estate information, including
(i) space utilization data values and (ii) cost utilization data
values of the comparable real estate,
means for storing building classification information, including
(i) a data classification of the real estate that is consistent
with generally accepted standards, and (ii) data classifications,
consistent with generally accepted standards, of buildings of the
comparable real estate,
means for storing area information, including (i) rent data, (ii)
vacancy data, (iii) absorption rate data, and (iv) area information
data of the business entities, and
means for storing financial, market and environmental risk information,
including (i) data representing an age of the real estate, (ii)
data representing locations of man made and naturally occurring
environmental hazards, (iii) data representing remaining financial
obligations and encumbrances of the Business Entity for the real
estate, and (iv) data representing the financial-obligations and
tenancy status of the Business Entity in the real estate, (v) tenancy
status and/or remaining time before a financial obligation of the
Business Entity for the real estate, is terminated; and/or other
measures of financial, market and environmental risks, as defined
by industry, professional and/or governmental organizations and
generally accepted in industry or professional practice,
processor means for determining numerical indicators of the real
estate by processing information in the database, the numerical
indicators including a space utilization indicator, a cost utilization
indicator, a grade indicator, an area indicator, and a risk indicator,
the processor means comprising
means for processing the space utilization indicator as a numerical
representation and as a function of (i) the square footage data,
(ii) the usage data, (iii) the employee data, (iv) the sales or
revenue data, (v) the space utilization data values, and
means for processing the cost indicator as a numerical representation
and as a function of (i) the square footage data, (ii) the rental
data, (iii) the usage data, (iv) the employee data, (v) the sales
or revenue data, and (vi) the cost data values,
means for processing the grade indicator as a numerical representation
and as a function of (i) the data classification, and (ii) the data
classifications,
means for processing the area indicator as a numerical representation
and as a function of (i) the rent data, (ii) the vacancy data, (iii)
the absorption rate data, (iv) the area information data, and (v)
transportation, infrastructure and demographic data,
means for processing the risk indicator as a numerical representation
and as a function of (i) the building age data, (ii) the man-made
and naturally occurring environmental hazard location data, (iii)
the data about remaining financial obligations and encumbrance of
the business entities for the real estate, and (iv) the data about
the tenancy status and remaining time of financial obligations of
the business entities in the real estate,
means for combining the indicators to produce an overall score
representing a quantitative evaluation of the Business Entity's
real estate condition, and to produce a report including the score
and analytical information about the Business Entity's real estate,
and
display means for displaying the report to the Customer.
19. A system for providing real estate and facilities information
to a Customer that uses, leases, buys, sells, owns, manages, consults
on, and/or advises on real estate, comprising:
a database for storing information about a Business Entity and
the Business Entity's real estate and about comparable real estate
of business entities which are similar to the Business Entity, the
information comprising space utilization and cost utilization information
about the real estate and about the comparable real estate,
processing means for analyzing the information the processing means
providing an overall score based upon an evaluation of the information,
and generating a report that includes the score and a quantitative
evaluation of the Business Entity's real estate condition; and
means for providing a print-out of the report.
20. A system according to claim 19, further comprising display
means for outputting data and analytical information about the Business
Entity's real estate; the display means displaying the report, the
report further including a name of the Business Entity, and at least
one of the following: (i) a portion of the data on the Business
Entity and the Business Entity's real estate, (ii) real estate ratios
representing the analyzed data, (iii) graphs of data comparing the
Business Entity's real estate to industry averages based on similar
standard industrial classification codes, and (iv) information describing
the outputted data and analytical information to a Customer of the
system.
21. A system according to claim 20, wherein the report further
includes a name of a real estate submarket, and at least one of
the following: (i) a total square feet for each of the different
types and uses of the real estate in the submarket, (ii) vacancy
rates for each of the different types and uses of the real estate
in the submarket, (iii) absorption rates for each of the different
types and uses of real estate in the submarket, (iv) rents for each
of the different types and uses of real estate in the submarket,
(v) terms of recent facility relocations and/or transactions for
selected occupants and locations in the real estate submarket, and
(vi) a list of real estate locations available for lease or sale
in the real estate submarket.
22. A system according to claim 20, further comprising means for
generating reports and related products that provide the following
decision support tools: (i) self analysis and comparison tools,
(ii) market and submarket data reports, (iii) available space reports,
maps and commercial listing services, (iv) comparable real estate
transactions lists and maps, (v) customer development tools, including
lease aging profiles and lease aging lists, (vi) computer software,
including staff, space and least negotiation models, and software
for self analysis, (vii) manulas and printed reference materials,
and future and derivative products.
Real estate description
BACKGROUND
Historically, big corporations and sophisticated users have had
an implicit strategy for their facilities portfolios. Increasingly,
they need explicit information on how they are performing in real
estate and occupancy cost management. All users--big and small alike--implement
their strategies through transactions. At each stage of the transaction
process--from scanning the market, to choosing buildings and spaces
within them, to negotiating the lease or buy component, to occupancy--there
are a number of participants who use information. Typically, the
transaction process is very compressed: even small firms make multi-million
dollar decisions in a few intensive months of review and negotiation.
But because they only do so sporadically, they do not apply the
same disciplines as in other business functions and usually lack
reliable, robust information to support their decisions.
For example, although a typical real estate commitment for a small
firm of twenty people is a $2 Million transaction over the life
of the lease or contract, few participants look at it that way:
the BROKER talks about it as a $20/SF "deal," the ARCHITECT
sees it as a 10,000 SF design, the MANAGER sees it as a $200,000/year
operating expense, and the CONTRACTOR sees it as a $300,000 project;
but the COMPANY should see it as a $2 Million business commitment.
In effect, there is, presently, no rigorous means for evaluating
real estate from the users' perspective, and for assisting those
users transacting business on such real estate. Further, the diverse
real estate nomenclature, the infrequency of the typical user's
transactions relating to real estate, and the wide scope of factors
affecting any given real estate "deal", inhibits effective
decision making. Unfortunately, a mistake by a company in such decisions
can be costly.
Accordingly, one object of the invention is to provide systems
and methods for evaluating real estate for purchase, lease, and/or
use by a business.
A further object of the invention is to provide a system for efficiently
assisting businesses in making real estate decisions and in a manner
which provides quantitative evaluation of factors associated with
a prospective real estate transaction.
These and other objects of the invention will be apparent in the
description which follows.
SUMMARY OF THE INVENTION
As used herein, "Score" is a quantitative evaluation
of a Business Entity's real estate condition. "Real Estate
Ratios" are used herein to describe a company's facilities
situation. "Market Condition", "Transaction data"
and "Facility Relocation data" are used herein to denote
options and constraints on facility actions. "Business Entity"
means any individual, organization, enterprise, business, company,
corporation, or partnership, including partnerships of multiple
corporations, that occupies real estate for non-residential use,
including for commercial, industrial, government and non-profit
functions, or which contemplates a real estate purchase, lease,
use, and/or rental. "Real Estate" means any real property,
including, without limitation, office, retail and industrial rental
space, a building, and multiple buildings for use by a business
entity, or one or more facilities or buildings that a business entity
occupies for the purpose of conducting its operations on a routine
and ongoing basis.
Also as used herein, "Use" means the specified, intended
or actual function and purpose for which a business entity employs
its real estate, including such functions as office work, manufacturing,
storage and retailing. "Utilization" means the efficiency
with which real estate is used by an occupant. For example, "Space
Utilization" means the efficiency by which the space of a real
estate is being employed, where high space utilization signifies
efficient use of space; and "Cost Utilization" means the
efficiency by which the cost of a real estate is being used, where
high cost utilization signifies low cost of space. "Market"
means a geographic region, e.g., the Boston metropolitan area, that
includes all buildings or potential buildings available for business
occupancy, with boundaries that are consistent with U.S. Census
standards and that are generally accepted definitions of metropolitan
areas. "Submarket" means a geographic area as a subset
of a Market and that includes buildings or potential buildings available
for business occupancy, with boundaries generally accepted by local
real estate professionals. "Grade" means the designated
quality of real estate on a relative scale of quality, from low
to high, generally accepted within a real estate market. "Rent"
means the annual amount paid by a business entity for rights to
occupy real estate. "Vacancy Rate" means the percentage
of square feet that is offered for lease in a market, submarket
or building. "Absorption" means the net reduction in vacant
square feet over a defined period (typically, twelve-months) resulting
from new tenants, new construction, and lease terminations. "Comparable
Real Estate" means real estate that is approximately equivalent
in Rent, Grade, Use and/or location to the business entity's Real
Estate. "Similar Business Entities" are those entities
which are compared to a Business Entity, such as those Business
Entities which have a standard industrial classification code, i.e.,
a SIC code, that is similar to the Business Entity. "Indicator"
means a quantitative measure of a particular factor affecting Real
Estate.
The Score is generally determined by five indicators of Amount,
Price, Grade, Area and Risk. Therefore, as used herein, "Amount"
means an indicator of a business entity's space utilization of real
estate; and it is based primarily upon square feet per employee,
and/or sales or revenues per square foot. "Price" means
an indicator of a business entity's cost utilization of real estate;
and it is based primarily upon the rent per square foot, the rent
per employee, and/or the rent to sales of the business entity in
that real estate. "Grade" means an indicator of the quality
of real estate, such as a building; and it is based primarily upon
generally accepted classification structure, such as Class A, B
or C properties. "Area" means an indicator of economic
attractiveness of the submarket where the real estate is located;
and it is based, for example, upon rents, vacancy, absorption rate
and/or other measures of economic attractiveness of a submarket.
"Risk" means an indicator of the financial, market and
environmental exposure of real estate and of the financial, market
and environmental risks associated with the employees and the business
entity's occupancy in the real estate. The Score composite of these
five indicators is also referred to as "The Apgar Score".
"Customer" is used to denote a user of the Invention,
such as an individual or Business Entity, which desires information
about Real Estate. Further, Real Estate is sometimes denoted as
"targeted" real estate to identify one or more particular
Real Estate locations under investigation. "Weighting Factor"
is used to denote an empirically determined adjustment for each
indicator, indicator component or measure used in the Score to reflect
the indicator's relative importance in the overall evaluation. "Measure"
is used to denote a quantitative and qualitative fact and/or calculation
from data about a Business Entity's real estate situation used to
determine values for score Indicators. "Tenancy Status"
is used to denote whether a business entity owns and occupies the
real estate, or whether the business entity leases or rents and
occupies the real estate, and the lime remaining on the lease(s)
or other contractual obligation(s). "Encumberances" is
used to describe whether a lien and/or any other contingent forfeiture
of ownership by the business entity is associated with financial
obligations of the business entity for the real estate, and the
time remaining of those obligations.
The invention provides, in one aspect, a method for evaluating
Real Estate for use by a Business Entity. The method includes the
steps of (A) storing Utilization information--including (i) square
footage data representing square footage of the Real Estate, (ii)
usage data characterizing the selected Use(s), (iii) cost data including
a Rental price of the Real Estate--about the Real Estate in a database,
(iv) data representing the number of employees of the business in
the real estate, and (v) data representing sales or revenues of
the Business Entity in the real estate; (B) storing data representing
Utilization values in the database from Business Entities which
are similar to the Business Entity; (C) determining a Utilization
Indicator of the Real Estate--wherein the Utilization Indicator
has a numerical representation and is a function of (i) the square
footage, (ii) the selected Use, (iii) the Utilization values, and
(iv) the cost data including the Rental Price--by processing information
in the database; (D) processing the numerical representation to
produce a Score representing a quantitative evaluation of the Real
Estate; and (E) communicating the Score to the Customer.
The invention thus provides information to business entities in
a dynamic and intelligent manner. It assumes that company decision-makers
need information that links real estate to the business in a meaningful
way. The invention further provides advantages in supporting business
planning and management with real estate information delivered through
hard copy reports, on-line service, computerized information transfers,
telephone consultation, and in-person consultation in at least six
Customer market segments referred to hereinafter as business entities:
businesses, investors, property developers/managers, real estate-related
intermediaries, government users, and all others. The invention
provides clear, unbiased, and objective information on such business
entities, thereby assisting Customers in making real estate decisions.
Without limitation, the preferred Customers of the invention include:
(1) business users, made up of large, mid-size and small companies,
which need information on rents, space utilization, and business/real
estate measures; (2) investors, including banks, pension funds and
insurance companies which provide the financing for most commercial
real estate, and need information for tenant retention, demand forecasts
and real estate affordability for tenants; (3) property developers/managers
who need information for tenant retention, demand forecasts and
affordability; (4) real estate intermediaries, including real estate
brokers, appraisers, accountants, lawyers, architects and other
real estate service providers, who need business/real estate measures
and lease expiration profiles for their own customer development;
and (5) government agencies and local governments that require user-based
assessments similar to business users.
The number of these potential Customers is substantial; and thus
the invention provides advantages to such Customers by providing
real estate evaluations in an objective, cost-effective, timely
and quantitative manner. It is estimated that in the United States
as many as 20 Million business, professional, non-profit and governmental
organizations use real estate for their operations. Further, almost
700,000 intermediaries--brokers, lawyers, architects, property managers,
consultants etc.--service these organizations; almost 50,000 institutions--banks,
pension funds, savings institutions--invest in real estate; and
as many as 100,000 government agencies lease, own and/or regulate
space. In addition, the invention provides useful information assessments
to Customers as the Business Entity's business mix changes, such
as when (1) competition focuses business managers' attention on
cost reduction and growth--including occupancy and location; (2)
re-engineering challenges assumptions including changes in real
estate types (office, industrial, retail) and building grades; (3)
technology improvements increase the potential for alternative locations
and building types; and (4) changes in capital markets structure
lead to reevaluation of real estate investments, including interest
rates, real estate supply and demand, and investment rates of return.
In another aspect, the invention supports three types of business
needs: (1) self analysis by a company evaluating real estate performance
over time and among peers, (2) decision support to identify essential
factors driving real estate decisions, and (3) customer development
by real estate intermediaries seeking customers.
Generally, the data needs for the invention include information
representative of rent, square feet, lease expiration, staffing
levels and sales. Thus, in another aspect, the invention includes
a database including information about one or more of the following:
(1) the annual rent paid by a company for its real estate, (2) the
square feet of the real estate occupied, (3) the expiration of the
Business Entity's real estate leases, (4) the number of employees
of the Business Entity in the real estate, and (5) the sales volumes
of the Business Entity in the real estate.
The database preferably includes other information, in accord with
further aspects of the invention, including: square footage data
representing a square footage of the real estate, usage data characterizing
the selected use, and rental data representing a rental price of
the real estate; space utilization data values, cost utilization
data values of comparable real estate, building classification information,
including a data classification of the real estate that is consistent
with local building standards, and data classifications, consistent
with local building standards, of buildings of the comparable real
estate; area information, including rent data, vacancy data, absorption
rate data, and area information data of other business entities;
financial, market and environmental risk information, including
data representing an age of a building at the real estate, data
representing locations of naturally occurring and man-made environmental
hazards; data representing the tenancy status and encumbrances of
the Business Entity for the real estate; and data representing real
estate supply, demand, pricing, regulation, transportation, infrastructure
and other economic conditions.
The preferred Customers of the invention are generally characterized
as follows: Large Customers have sophisticated, on-going information
needs to manage large, dynamic portfolios; while other Customers
have only periodic information needs linked to real estate transactions.
All Customers may receive a broad hierarchy of information, including
printed manuals, definitions of key indicators, analytical reports
of internal performance and market trends, simplified and detailed
analyses with the Score, specific inquiries on individual facilities,
and decision scenario modeling software.
Three types of analyses access information on the database: (1)
trend analysis of key facilities measures over time within the firm,
(2) benchmarks of key indicators across firms within an industry,
and (3) the analyses associated with the Score. According to another
aspect of the invention, software is also available for Customers
to do their own "what-if" analyses.
Self analysis reports enables Customers to identify "hidden"
cost trends. A report for one company, for example, shows that while
occupancy cost per square foot fell 20% over a six year period,
it added space twice as fast as staff. This growth mismatch resulted
in a 50% increase in square feet per person and a one third excess
in annual occupancy cost. The invention can thus be used to highlight
such trends before they spiral out of control.
In still another aspect of the invention, the Score includes five
basic indicators of real estate health: Amount, Price, Grade, Area
and Risk. Preferably, each of these indicators is scaled for a total
potential score of 10. Low scores highlights the need for the Business
Entity's top management to focus on real estate issues. This Score
can be used to evaluate a Business Entity's real estate portfolio
and to report real estate condition to the decision makers and/or
Customer. Accordingly, the invention is suitable for those Customers/decision-makers
who are not real estate specialists and who find real estate issues
complicated and confusing.
The invention further provides, in another aspect, support information
for each Business Entity group, including: (1) Market Overviews
of the submarkets in a metropolitan area, sourced and updated periodically;
and (2) Facility Relocations and/or Transactions Reports in each
submarket sourced from a business database and updated during the
normal reporting cycle using sources such as the National Change
of Address database and/or revisions to lease expiration dates.
To provide this type of submarket data, the invention provides submarket
boundaries for a selected number of markets.
The display of information features, in accord with another aspect
of the invention, seven sections: (1) an identification section
for specifying the company, location and operational highlights;
(2) a Score section; (3) a detailed real estate data section; (4)
a key real estate ratios section which relate real estate to business
measures; (5) a market conditions section reporting the state of
the surrounding real estate market; (6) a facilities relocations
and/or transactions section that lists selected activity in the
surrounding real estate market; and (7) definitions of real estate
terms so the report is usable by real estate experts and non-experts
alike.
On a market-by-market basis, and in accord with another aspect
of the invention, the database is periodically updated, for example
on a quarterly or semi-annual basis, with information of those leases
due to expire in the next year, along with their square footage,
current rent, address, contact and phone numbers.
These and other advantages and aspects of the invention are evident
in the description which follows and in the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates process methodology and a system, according
to the invention, for storing and accessing information to evaluate
selected real estate;
FIG. 2 illustrates apparatus for processing and converting request
signals and information into signals representative of an evaluation
of real estate, in accord with the invention; and
FIGS. 3-21 show process flow methodology, according to the invention,
for determining factors, measures, ratios, and other real estate
indicators for use in determining real estate condition and situation.
DETAILED DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates real estate 12, shown illustratively as two
buildings 12a, 12b, which is under consideration for a selected
use by a Business Entity. In accord with the invention, a database
14 stores selected information about the real estate 12 so that
the Business Entity can effectively and objectively evaluate the
real estate 12. Specifically, the database 14 includes memory section
16, e.g., magnetic or optical disc storage, to store selected information
which is processed in accord with the methods described below. The
memory section 16 holds information about the real estate 12, including
utilization information such as square footage, usage data indicating
the selected use, e.g., a rental property, and cost data including
a rental price of the real estate 12.
In order to provide an evaluation of the real estate, information
about similar business entities and properties are needed. Thus,
the memory section 16 also holds utilization values which characterize
business entities which are similar to the Business Entity. One
manner in which this similarity is determined is by comparing the
standard industrial classification codes (SIC) between the Business
Entity and other business entities (for example, it is assumed that
entities which have the same SIC as the Business Entity are "similar").
The utilization information and values are communicated to the
computer 16, via communication line 17, and are processed by the
computer 18 to determine a utilization indicator. The utilization
indicator has a numerical representation, such as a number, and
is a function of the square footage of the real estate 12, the selected
use of the real estate 12, the utilization values of the business
entities, and the cost data. In accord with the invention, the computer
18 processes the utilization indicator to provide a score evaluating
the real estate 12, such as described in greater detail below. The
score is then communicated to a Customer of the invention by way
of the display 20, printer 22, and/or telephone and/or facsimile
and/or modem device.
Preferably, the utilization information specifically includes space
utilization information and cost utilization information. Respectively,
such information is gathered, in accord with the invention, by accessing
and extracting space utilization data records and cost utilization
data records from a business database 24, such as but not limited
to those commercially available business databases from Dun &
Bradstreet.TM., and/or TRW.TM., and/or Equifax.TM.. The database
24 can be manually downloaded to the database 14; or, alternatively,
the database 24 can be connected for automatic download of selected
information to the database 14 on an as-needed basis, such as known
to those skilled in the art. For example, information regarding
certain submarkets and SIC codes can be imported to the database
14 from the database 24 when such information changes, to reflect
the most recent real estate conditions and to provide the most accurate
real estate evaluation to a Customer.
The invention preferably generates a score that includes factors
other than utilization information. For example, the score--as a
minimum--includes utilization information relating to cost and space,
and preferably includes information relating to one of grade, area
and risk.
Accordingly, the score provides for up to five basic indicators
of a company's real estate condition--Amount, Price, Grade, Area
and Risk. Preferably, each of the five indicators is scaled for
a total potential score of 10, thereby providing an objective evaluation
of the real estate for the Business Entity. For example, a score
of 5 or below generally highlights the need for the Business Entity's
management to focus on real estate issues.
In accord with certain embodiments of the invention, each section
below describes in detail the steps including the algorithm for
determining a numerical indicator for each of these five indicators.
As also described below, the indicators are processed to form a
composite score.
Amount
Amount is an indicator of space utilization by a Business Entity
of the selected real estate location. The amount evaluation is based
primarily on at least one of two measures: (i) the square-footage
per employee (SF/Employee) and/or (ii) the sales per square foot
(Sales/SF). Higher indicators are assigned to Business Entities
with higher than average space utilization for similar entities;
and lower indicators are assigned to Business Entities with lower
than average space utilization for similar entities.
The preferred data stored in the database to determine the amount
includes the square feet and the number of employees for the Business
Entity; and the square feet and number of employees for all companies
within the SIC codes that are similar to the SIC code of the Business
Entity. The source for the space utilization data records is provided
by commercially available business databases, in addition to information
provided by the Business Entity, such as the number of employees-in
the real estate.
According to one embodiment of the invention, the algorithm for
calculating amount is as follows. Note that in the embodiment described
below, the total amount indicator is broken into a first and second
amount indicator:
Steps 1-6 are performed periodically, e.g., monthly or quarterly:
1. Extract records with SIC code, square feet, and number of employees
from business database.
2. Calculate Square Feet/Employee for each record extracted.
3. Sort the resulting list of results by SIC code.
4. Calculate the average SF/Employee for each SIC code.
5. Calculate the standard deviation of the distribution of SF/Employee
for each SIC code.
6. Develop a list of SIC codes with average SF/Employee and standard
deviation for each.
Steps 7-13 are performed for each Business Entity as needed:
7. Extract the Business Entity's SIC code, Square Feet and Employees
from the Business Entity record.
8. Calculate the SF/Employee for the Business Entity.
9. Extract the average SF/Employee and standard deviation for the
Business Entity's SIC code.
10. Calculate the Business Entity's variance (CV): CV=user SF/Employee-SIC
average SF/Employee.
11. If the absolute value of the CV is less than standard deviation
(i.e. the variance of most of the business entities in the SIC code),
then the Business Entity is near the average, and a numerical representation
of a medium score is assigned to the first amount indicator.
12. If the CV is negative and its absolute value is greater than
or equal to the standard deviation, then the Business Entity is
using space better than the industry average, and a numerical representation
of a high score is assigned to the first amount indicator.
13. If the CV is positive and greater than or equal to the standard
deviation then the Business Entity's space utilization is lower
than the industry average, and a numerical representation of a low
score is assigned to the first amount indicator.
Steps 14-19 are performed periodically, e.g., monthly or quarterly:
14. Extract records with SIC code, square feet, and annual sales
from business database.
15. Calculate Sales/Square Foot for each record extracted.
16. Sort the resulting list of results by SIC code.
17. Calculate the average Sales/Square Foot for each SIC code.
18. Calculate the standard deviation of the distribution of Sales/Square
Foot for each SIC code.
19. Develop a list of SIC codes with average Sales/Square Foot
and standard deviation for each.
Steps 20-26 are performed for each Business Entity as needed:
20. Extract the user's SIC code, Square Feet and Annual Sales from
the Business Entity record.
21. Calculate the Sales/SF for the Business Entity.
22. Extract the average Sales/SF and standard deviation for the
user's SIC code.
23. Calculate the Business Entity's variance (CV): CV=user Sales/SF-SIC
average Sales/SF.
24. If the absolute value of the CV is less than standard deviation
(i.e. the variance of most of the business entities in the SIC code),
then the Business Entity is near the average, and a numerical representation
of a median score is assigned to the second amount indicator.
25. If the CV is positive and its absolute value is greater than
or equal to the standard deviation, then the Business Entity's space
utilization is higher than the industry average, and a numerical
representation of a high score is assigned to the second amount
indicator.
26. If the CV is negative and greater than or equal to the standard
deviation then the Business Entity's space utilization is lower
than the industry average, and a numerical representation of a low
score is assigned to the second amount indicator.
Steps 27-28 are performed for each Business Entity as needed:
27. The assigned scores for the first and second amount indicators
are adjusted by weighting factors.
28. The adjusted first and second amount indicators are combined
to determine an overall numerical representation for the total amount
indicator.
Price
Price is an indicator of cost utilization at the Business Entity's
location. Evaluation is based upon at least one of three measures:
(i) Rent/SF, (ii) Rent/Employee, and/or (iii) Rent/Sales. Each of
the three measures is evaluated independently, and combined with
appropriate weighting factors to determine the total price indicator.
Higher indicators are assigned to business entities with higher
than average industry real estate cost utilization, and lower indicators
are assigned to business entities with lower than average industry
real estate costs utilization.
The preferred data stored in the database to determine the price
indicator includes the rent, square feet, and sales for the Business
Entity, and the rent, square feet, and sales for all companies within
the SIC codes that are similar to the SIC code of the Business Entity.
The source for the price/cost data records is provided by commercially
available business databases, in addition to information provided
by the Business Entity.
According to one embodiment of the invention, the algorithm for
calculating the price indicator is as follows. Note that in the
embodiment described below, the total price indicator is broken
into a first, second, third and fourth price indicator:
Steps 1-6 are performed periodically, e.g, monthly or quarterly:
1. Extract all records with SIC code, Square Feet and Rent from
the business database.
2. Calculate Rent/Square Feet for each record extracted.
3. Sort the resulting list of results by SIC code.
4. Calculate the average Rent/SF for each SIC code.
5. Calculate the standard deviation of the distribution of Rent/SF
for each SIC code.
6. Develop a list of SIC codes with average Rent/SF and standard
deviation for each.
Steps 7-13 are performed for each Business Entity as needed:
7. Extract the Business Entity's SIC code, Square Feet and Rent
from the data record.
8. Calculate the Rent/SF for the Business Entity.
9. Extract the average SF/Employee and standard deviation for the
Business Entity's SIC code.
10. Calculate Business Entity's variance (CV): CV Business Entity's
Rent/SF-SIC average Rent/SF.
11. If the absolute value of the CV is less than the standard deviation
(i.e. the variance of most of the companies in the SIC code), then
the Business Entity's Rent/SF is near the industry average, and
a numerical representation of a medium score is assigned to the
first price indicator.
12. If the CV is negative and its absolute value is greater than
or equal to the standard deviation, then the Business Entity's Rent/SF
is lower than the industry average, and a numerical representation
of a high score is assigned to the first price indicator.
13. If the CV is positive and greater than or equal to the standard
deviation, then the Business Entity's Rent/SF is higher than the
industry average, and a numerical representation of a low score
is assigned to the first price indicator.
Steps 14-19 are performed periodically, e.g., monthly or quarterly:
14. Extract all records with SIC code, Rent and Employees from
the business database.
15. Calculate Rent/Employee for each record extracted.
16. Sort the resulting list of results by SIC code.
17. Calculate the average Rent/Employee for each SIC code.
18. Calculate the standard deviation of the distribution of Rent/Employee
for each SIC code.
19. Develop a list of SIC codes with average Rent/Employee and
standard deviation for each.
Steps 20-26 are performed for each Business Entity as needed:
20. Extract the Business Entity's SIC code, the number of employees
of the Business Entity in the real estate (hereinafter referred
to as Employees/This Location), and Rent from the Business Entity's
data record.
21. Calculate the Rent/Employee for the Business Entity.
22. Extract the Average Rent/Employee and standard deviation for
the Business Entity's SIC code.
23. Calculate Business Entity's (CV): CV=Business Entity's Rent/Employee-SIC
Average Rent/Employee.
24. If the absolute value of the CV is less than the standard deviation
(i.e. the variance of most of the companies in the SIC code), then
the Business Entity's Rent/Employee is near the industry average,
and a numerical representation of a medium score is assigned to
the second price indicator.
25. If the CV is negative and its absolute value is greater than
or equal to the standard deviation, then the Business Entity's Rent/Employee
is lower than the industry average; and a numerical representation
of a high score is assigned to the second price indicator.
26. If the CV is positive and greater than or equal to the standard
deviation, then the Business Entity's Rent/Employee is higher than
the industry average; and a numerical representation of a low score
is assigned to the second price indicator.
Steps 27-32 are performed periodically, e.g., monthly or quarterly:
27. Extract all records with SIC code, Rent and Sales from the
business database.
28. Calculate Rent/Sales for each record extracted.
29. Sort the resulting list of results by SIC code.
30. Calculate the average Rent/Sales for each SIC code.
31. Calculate the standard deviation of the distribution of Rent/Sales
for each SIC code.
32. Develop a list of SIC codes with average Rent/Sales and standard
deviation for each.
Steps 33-39 are performed for each Business Entity as needed:
33. Extract the Business Entity's SIC code, Rent and Sales from
the Business Entity's data record.
34. Calculate the Rent/Sales for the Business Entity.
35. Extract the Average and Rent/Sales for the Business Entity's
SIC code.
36. Calculate Business Entity's variance (CV): CV=Business Entity's
Rent/Sales-SIC Average Rent/Sales.
37. If the absolute value of the CV is less than the standard deviation
(i.e. the variance of most of the companies in the SIC code), then
the Business Entity's Rent/Sales is near the industry average, and
a numerical representation of a median score is assigned to the
third price indicator.
38. If the CV is negative and its absolute value is greater than
or equal to the standard deviation, then the Business Entity's Rent/Sales
is better than the industry average; and a numerical representation
of a high score is assigned to the third price indicator.
39. If the CV is positive and greater than or equal to the standard
deviation, then the Business Entity's Rent/Sales is higher than
the industry average; and a numerical representation of a low score
is assigned to the third price indicator.
Steps 40-41 are performed periodically, e.g. monthly or quarterly:
40. Extract Average Rent/SF for each property type within each
submarket from an external Submarket Database.
41. Develop a list of average Rent/SF for each property type within
each submarket.
Steps 42-49 are performed for each Business Entity as needed:
42. Extract the Business Entity's SIC code, Square Feet and Rent
from the Business Entity's data record.
43. Determine Business Entity's property type from SIC code and/or
other available data sources, and Submarket from address.
44. Calculate the Rent/SF for the Business Entity.
45. Extract the Rent/SF for Business Entity's property type from
Submarket Rent/SF list.
46. Calculate variance between Business Entity's Rent/SF and Submarket
Rent/SF.
47. If the variance is less than a threshold (to be developed empirically)
of the average Submarket Rent/SF, then the Business Entity's Rent/SF
is near the submarket average, and a numerical representation of
a medium score is assigned to the fourth price indicator.
48. If the CV is negative and greater than or equal to a threshold
(to be developed empirically) of the average Submarket Rent/SF,
then the Business Entity's Rent/SF is lower than the submarket average;
and a numerical representation of a high score is assigned to the
fourth price indicator.
49. If the CV is positive and greater than or equal to a threshold
(to be developed empirically) of the average Submarket Rent/SF,
then the Business Entity's Rent/SF is higher than the submarket
average; and a numerical representation of a low score is assigned
to the fourth price indicator.
Steps 50-51 are performed for each Business Entity as needed:
50. The assigned scores for the first, second, third and fourth
price indicators are adjusted by weighting factors.
51. The adjusted first, second, third and fourth price indicators
are combined to determine the total price indicator.
Grade
Grade is an indicator of the quality of facility for the selected
real estate location. Evaluation is preferably based on generally
accepted classification systems. Building grade is less important
than price and amount; therefore it is not weighted as heavily.
A higher grade indicator is not rewarded for space that is of a
significantly higher grade than industry average, because of the
uncertain value of such higher quality space (e.g. occupying Class
A space when industry practice is to occupy Class C space).
The preferred data stored in the database to determine the grade
includes building addresses, and building classifications for such
buildings, and other information relevant to practice within the
SIC/Industry. Information for the grade data records is provided,
for example, by real estate data vendors, available business databases,
the U.S. Postal Service and by On-Site Inspectors, in addition to
information provided by the business entity.
According to one embodiment of the invention, the algorithm for
calculating grade is as follows:
Steps 1-11 are performed periodically, e.g. quarterly or annually:
1. Develop listing of commercial building addresses in a market
from a business database or external source, such as the U.S. Postal
Service the phone company, and real estate data vendors.
2. Select a sample of buildings for survey. Sample size is contingent
on resources/time available for survey.
3. Using a variety of sources, including direct inspection, real
estate professionals or real estate data vendors, assign, 2 or 3
a numerical representation of a uniform grade to each building based
upon the generally accepted classification systems. Update the electronic
list of addresses with grades.
4. Extrapolate a grade to each building on the list that was not
surveyed based on grades of surrounding surveyed buildings.
5. Develop a list of addresses and grades.
6. Extract from the business database all companies/records with
addresses that match the address/grade list.
7. Assign each extracted record a building grade based on the grade
assigned to its address.
8. Sort company records by SIC code.
9. Calculate average building grade by SIC Code.
10. Calculate standard deviation by SIC Code.
11. Develop a list of SIC codes with average building grade and
standard deviation for each.
The following steps 12-18 are performed for each Business Entity
as needed:
12. Extract the Business Entity's building grade and SIC code from
the Business Entity's data record. If the building grade was not
surveyed as part of the periodic review in step 3 above then proceed
to step 13 below; or else proceed to step 14 below.
13. Inspect the Business Entity's real estate and assign a building
grade. Update the Business Entity's data record and the address/grade
list for inspection, as well as any other company records for that
address.
14. Extract the average building grade and the standard deviation
of the distribution of building grade for the Business Entity's
SIC code.
15. Calculate Business Entity's variance (CV): CV=Business Entity's
building grade-SIC average building grade.
16. If the absolute value of the CV is less than the standard deviation
(i.e. the variance of most of the companies in the SIC code), then
the Business Entity is near the average; and a numerical representation
of a median score is assigned to the grade indicator.
17. If the CV is positive and greater than or equal to the standard
deviation, then the Business Entity is in a higher grade building
than industry' average; and a numerical representation of a high
score is assigned to the grade indicator.
18. If the CV is negative and its absolute value is greater than
or equal to the standard deviation, then the Business Entity occupies
a facility that is lower in grade than the industry practice; and
a numerical representation of a low score is assigned to the grade
Area
Area is an indicator of the economic attractiveness of the submarket
in which the Business Entity is located. Evaluation (Submarket Ranking)
is based on Rents, Vacancy and Absorption rates, transportation
and infrastructure measures, demographic profiles, and other objective
and subjective measures of submarket attractiveness. Area is less
important than Amount and Price and is not weighted as heavily.
As with grade, a higher area indicator is not rewarded for submarkets
that are of a significantly higher rating than industry average,
because of the uncertain value of such higher quality locations.
The preferred data stored in the database to determine the area
indicator includes Submarket Rent/SF, Vacancy %, Absorption Rate,
Building addresses, and Submarket conditions data. The source for
the data records supporting the area indicator determination is
provided by real estate data vendors, Market data, Submarket Review,
and commercially available business databases, in addition to information
provided by the Business Entity.
According to one embodiment of the invention, the algorithm for
calculating area is as follows:
Steps 1-4 are performed periodically, e.g. quarterly or annually:
1. Define and Develop list of submarkets for each Market.
2. Calculate Rent/SF, Vacancy % and Annual Absorption Rate for
each Submarket within a Market.
3. Rank, according to each SIC code, each Submarket relative to
other Submarkets within its Market by evaluating the Submarket's
attractiveness based on at least one of the following measures:
Rent/SF, Vacancy %, Annual Absorption Rate, transportation and infrastructure
measures, demographic profiles and other objective and subjective
measures.
4. Develop a list of SIC codes, Submarkets and Submarket Rankings.
Determine the Median Ranking for each Market and SIC code.
Steps 5-10 are performed for each Business Entity as needed:
5. Extract the Business Entity's Submarket name and SIC code from
the Business Entity data records.
6. Extract the Business Entity's Submarket Ranking and the Median
Market Ranking for the business entity's SIC code.
7. Calculate the Business Entity's variance (CV): CV=Business Entity
Submarket Ranking-median Market Ranking.
8. If the CV is less than a threshold (to be determined empirically)
of the submarket rankings, then the Business Entity is near the
average; and a numerical representation of a medium score is assigned
to the area indicator.
9. If the CV is positive and is greater than or equal to a threshold
(to be determined empirically) of the submarket rankings, then the
Business Entity is in a stronger submarket than industry average;
and a numerical representation of a high score is assigned to the
area indicator.
10. If the CV is negative and its absolute value is greater than
a threshold (to be determined empirically) of the submarket rankings,
then the Business Entity's submarket is economically weaker than
the industry average; and a numerical representation of a low score
is assigned to the area indicator.
Risk
Risk is an indicator of the financial, market and environmental
exposure of the Business Entity in owning and/or leasing and/or
using the real estate at the location. Evaluation is based on at
least one of (i) the location's proximity to naturally occurring
and man-made environmental hazards, such as toxic waste sites and
radon sites as registered with the Environmental Protection Agency
(EPA); (ii) other hazardous indicators, such as asbestos exposure,
or building age for determining asbestos exposure, if the presence
of asbestos in a real estate is uncertain; (iii) the tenancy status
of the Business Entity in the real estate in comparison to other
business entities within a similar SIC code; (iv) the financial
encumberances of the Business Entity for the real estate; and (v)
other measures that indicate the financial, market and/or environmental
risks associated with the real estate.
The preferred data stored in the database to determine the area
includes, for example, the building age, environmental sites, radon
exposure and sites, tenancy status and financial encumberances of
the real estate. The source for the data records supporting the
risk indicator determination is provided by databases including
building age, zip code, environmental sites, radon sites, and the
EPA, in addition to information provided by the Business Entity.
According to one embodiment of the invention, the algorithm for
calculating a risk indicator is as follows(Note that in the embodiment
described below, the total risk indicator is broken into a first,
second, third and fourth risk indicator):
Steps 1 and 2 are performed periodically, e.g. quarterly or annually:
1. Develop a list of addresses or address codes that contain EPA
registered Toxic Waste sites from government published sources.
2. Develop a list of addresses or address codes that contain EPA
registered Radon sites from government published sources.
Steps 3-12 performed for each Business Entity as needed:
3. Extract the Business Entity's address or address code and building
age from the Business Entity's data record.
4. Using an appropriate geographic measurement system, such as
Zip Codes or Geocoding, determine the distance between the nearest
Toxic Waste site address and the Business Entity's address or address
code.
5. If the distance is greater than a threshold (to be developed
empirically) of the safe distance, then the Business Entity is not
significantly exposed to toxic waste risks; and a numerical representation
of a high score is assigned to the first risk indicator.
6. If the distance is smaller than a threshold (to be developed
empirically) of the safe distance, then the Business Entity may
be exposed to toxic waste risks; and a numerical representation
of a low score is assigned to the first risk indicator.
7. Using an appropriate geographic measurement system, such as
Zip Codes or Geocoding, determine the distance between the nearest
Radon site address and the Business Entity's address or address
code.
8. If the distance is greater than a threshold (to be developed
empirically) of the safe distance, then the Business Entity is not
significantly exposed to Radon risks; and a numerical representation
of a high score is assigned to the second risk indicator.
9. If the distance is smaller than a threshold (to be developed
empirically) of the safe distance, then the Business Entity may
be exposed to Radon risks; and a numerical representation of a low
score is assigned to the second risk indicator.
10. If the real estate at the location is known not to contain
Asbestos, or if the real estate at the location was built or last
renovated before 19XX (a year dependent upon state codes, and local
building practices relevant to the location of the real estate),
then it probably does not contain asbestos; and a numerical representation
of a high score is assigned to the third risk indicator.
11. If the real estate at the location was built or last renovated
after 19YY, then it probably does not contain asbestos; and a numerical
representation of a high score is assigned to the third risk indicator.
12. If the building is known to contain asbestos, or if the building
was constructed or last renovated after 19XX but before 19YY (a
period of years when local conditions mandated extensive use of
asbestos materials), then it may contain asbestos; and a numerical
representation of a low score is assigned to the third risk indicator.
Steps 13-15 are performed periodically, e.g. monthly or quarterly:
13. Extract all records with SIC code and tenancy status from the
business database.
14. Calculate the majority tenancy status for each SIC code (i.e.
for each SIC code, determine whether most business entities own
real estate or lease real estate).
15. List the majority tenancy status for each SIC code.
Steps 16-21 are performed for each Business Entity as needed:
16. Extract the Business Entity's SIC code, tenancy status, and
encumberances.
17. Extract the majority tenancy status for the Business Entity's
SIC code from the list developed in step 16.
18. If the Business Entity's tenancy status for the real estate
is as a lessee, then the Business Entity is deemed not to be exposed
to financial risk for the real estate, and a numerical representation
of a high score is assigned to the fourth risk indicator.
19. If the Business Entity's tenancy status for the real estate
is as an owner, and the majority tenancy status for the Business
Entity's SIC code is as a lessee, then the Business Entity is deemed
to be exposed to financial risk for the real estate and a numerical
representation of a low score is assigned to the fourth risk indicator.
20. If the Business Entity's tenancy status for the real estate
is as an owner, the majority tenancy status for the Business Entity's
SIC code is as an owner, and the real estate of the Business Entity
is not encumbered, then the Business Entity is deemed to have limited
exposure to financial risk for the real estate, and a numerical
representation of a high score is assigned to the fourth risk indicator.
21. If the Business Entity's tenancy status for the real estate
is as an owner, the majority tenancy status for the Business Entity's
SIC code is as an owner, and the real estate of the Business Entity
is encumbered, then the Business Entity is deemed to be exposed
to financial risk for the real estate, and a numerical representation
of a low score is assigned to the fourth risk indicator.
Steps 22-23 are performed for each Business Entity as needed:
22. The assigned scores for the first, second, third and fourth
risk indicators are adjusted by weighting factors.
23. The adjusted first, second, third and fourth risk indicators
are combined to determine the total risk indicator.
FIG. 2 illustrates a system constructed according to the invention
for polling and processing certain of the information stored in
a database to provide real estate evaluations as described herein.
Specifically, the database 14' stores information which can be processed
in accord with the several real estate indicators of real estate
condition--amount, price, grade area and risk. A computer 18' includes
a microprocessor subsection 30 which (i) accepts and interprets
signals representing commands from a user at the computer 18' and
which (ii) polls for information from the database 14' in response
to the command signals. If the desired information is not within
the database 14', the subsection 30 polls for the information from
the business database 24'. The communication between the two databases
14' and 24' can be accomplished through various communication networks,
including cabling 32, or other means, such as modems, telephone
lines and the like.
The subsection 30 thus responds to user command inputs and generates
polling signals to the database 14'. The user inputs typically correspond
to information about the Business Entity and the real estate, such
as the location and SIC code of the Business Entity. Preferably,
for any given transaction, information about the Business Entity
is stored in memory element 34; and such information typically includes
the name of the Business Entity, the number of employees of the
Business Entity in the real estate and/or the sales expected or
generated in the real estate, and the type of business run by the
Business Entity in the real estate 12.
Once the appropriate polling signals are generated to the database
14', information is collected from the database 14' and transferred
back to the subsection 30 for internal storage in the computer's
memory element 36. This transferred information typically includes
information about the real estate, such as its location, geographic
vicinity, submarket, market, and the other specific information
detailed above and associated with the real estate indicators.
The subsection 30 also generates request signals to the database
14' for informational data records about Business Entities which
are similar to the Business Entity. These request signals can be
manually input to the computer via Customer inputs, or, preferably,
the request signals are automatically generated as a function of
information stored in memory element 34. For example, once the location
and the type of business is loaded into memory 34, the computer
18' can be programmed to determine the SIC code of the business;
and thus the request signals can include a request for information
about similar business entities based upon at least one signal which
identifies the SIC code.
Informational data records about the similar business entities
is likewise stored in memory element 38. Each record, corresponding
to one real estate of each of the entities (the term "one real
estate" is used to denote one particular real estate, other
than the specific real estate under investigation, that is used,
owned, or otherwise occupied by one of the business entities that
is similar to the Business Entity), includes information about utilization
values for space and cost utilization. Accordingly, space utilization
information preferably includes square footage per employee, and
sales or revenue of the Business Entity in the real estate; while
cost utilization information preferably includes the number of employees
occupying the real estate, the square footage, the rental costs,
and the sales or revenue of the Business Entity in the real estate
per rental price of the real estate.
Information within the memory elements 34, 36, 38 is processed
by process actuators 40a-40e to determine the indicators as described
above: actuator 40a processes signals representative of amount information
to determine and generate an amount indicator signal; process actuator
40b processes signals representative of cost information to determine
and generate a cost (or price) indicator signal; process actuator
40c processes signals representative of grade information to determine
and generate a grade indicator signal; process actuator 40d processes
signals representative of area information to determine and generate
an area indicator signal; and process actuator 40e processes risk
information to determine and generate a risk indicator signal.
A score process section 50 combines the several indicator signals
from the actuators 40a-40e to process and generate a score signal,
which is representative of the score information as described herein.
The score signal is then transmitted to a display/print manager
52 which converts the score signal to information readable to a
human or electronic user and transmits such readable information
via communication line 51 to a display, e.g., the display 20 of
FIG. 1, or to a printer, e.g., the printer 22 of FIG. 1, to communicate
the score to a user. Preferably, the score and other information
concerning the score indicators, including real estate factors,
measures, and ratios, are provided to the Customer at a printer
or a facsimile device to provide a hard copy report detailing and
summarizing the analysis of the particular real estate.
It is important to note that not all indicators, and hence actuators
40a-40e, are needed to produce a score for use by a Customer. For
example, the utilization indicator signal can be processed only
from selected information relating to space (i.e., amount) utilization
and cost (i.e., price) utilization indicators. In such a case, the
process section 50 processes the space and cost utilization indicator
signals to generate a score from only the measures of cost and space.
(i.e., price) utilization indicators. In such a case, the process
section 50 processes the space and cost utilization indicator signals
to generate a score from only the measures of cost and space.
The system will produce various reports, enabling empirical data
analysis for system upgrade, marketing and other uses to be determined
(e.g. derivative products).
It is also important to note that the process may be applied to
a portfolio of multiple real estate facilities to generate a "portfolio
score" in addition to a score for a real estate comprised of
a single location.
The process flow, algorithms, and calculations for determining
the several indicators--are described in more detail below and in
connection with FIGS. 3-21
FIG. 3 represents a process flow chart for determining the amount
indicator for a use corresponding to sales per square foot. As above,
a business database 2" provides records 102 including an SIC
code, square feet and sales (revenue) information. This information
is processed (block 104) to calculate the sales per square foot
for each record, sorted (block 106) by SIC code, and processed (block
108) to calculate the standard deviation and average of the records
by SIC code. Thereafter, a list is generated (block 110) to provide
an average and standard deviation for each SIC code. One or more
entries of standard deviation and average from the list are selected
(block 112) to correspond to the SIC code of the Business Entity.
The database 14" likewise provides information about the Business
Entity. Specifically, the database 14" provides records 116
including square footage of the real estate, the SIC code of the
Business Entity, and the sales (expected or actual) of the Business
Entity in the real estate. This information is processed (block
118) to calculate the sales per square foot in the real estate.
The information processed from database 24" and database 14"
is combined (block 114) to calculate the variance. The variance
is compared (block 120) to determine whether to assign a high (block
122), medium (block 125), or low (block 124) indicator value "A"
to the amount. This value "A" is input and further processed
as indicated in the several figures below.
FIG. 4 represents a process flow chart for determining the amount
indicator for a use corresponding to square feet per employee. As
above, a business database 24" provides records (block 126)
including an SIC code, square feet and number of employees. This
information is processed (block 128) to calculate the square foot
per employee for each record, sorted (block 130) by SIC code, and
processed (block 132) to calculate the standard deviation and average
of the records. Thereafter, a list is generated (block 134) to provide
an average and standard deviation for each SIC code. One or more
entries of standard deviation and average from the list are selected
(block 136) to correspond to the SIC code of the Business Entity.
The database 14" likewise provides information about the Business
Entity. Specifically, the database 14" provides records (block
140), including square footage of the real estate, the SIC code
of the Business Entity, and the number of employees of the real
estate. This information is processed (block 142) to calculate the
square foot per employee at the real estate.
The information processed from database 24" and database 14"
is combined (block 138) to calculate the variance. The variance
is compared (block 144) to determine whether to assign a high (block
146), medium (block 149), or low (block 148) indicator value "B"
to the amount. This value "B" is input and further processed
as indicated in the several figures below.
FIG. 5 represents a process flow chart for determining the total
amount indicator from the two uses illustrated in the process flow
charts of FIGS. 3 and 4. The value "A," from FIG. 3, is
input (block 150), weighted (block 152) to produce a weighted indicator
(block 154) that is added to a similarly processed "B"
input, from FIG. 4. Thus "B" is input (block 156), weighted
(block 158) to produce a weighted indicator (block 160) based upon
sales per square foot. The two weighted values of blocks 154 and
160 are combined to produce the total amount indicator (block 162),
which is output and denoted as "P1."
FIG. 6 represents a process flow chart for determining the price
indicator for a use corresponding to rent per square foot. A business
database 24" provides records 166 including an SIC code, square
feet and rent information. This information is processed (block
168) to calculate the rent per square foot for each record, sorted
(block 170) by SIC code, and processed (block 172) to calculate
the standard deviation and average of the records by SIC code. Thereafter,
a list is generated (block 174) to provide an average and standard
deviation for each SIC code. One or more entries of standard deviation
and average from the list are selected (block 176) to correspond
to the SIC code of the Business Entity.
The database 14" likewise provides information about the Business
Entity. Specifically, the database 14" provides records (block
178), including square footage of the real estate, the SIC code
of the Business Entity, and the rent of the real estate. This information
is processed (block 180) to calculate the rent per square foot at
the real estate.
The information processed from database 24" and database 14"
is combined (block 182) to calculate the variance. The variance
is compared (block 184) to determine whether to assign a high (block
186), medium (block 188), or low (block 190) indicator value "D"
to the amount. This value "D" is input and further processed
as indicated in the several figures below.
FIG. 7 represents a process flow chart for determining the price
indicator for a use corresponding to rent per employee. As above,
a business database 24" provides records (block 192), including
an SIC code, rent and number of employees. This information is processed
(block 194) to calculate the rent per employee for each record,
sorted (block 196) by SIC code, and processed (block 198) to calculate
the standard deviation and average of the records. Thereafter, a
list is generated (block 200) to provide an average and standard
deviation for each SIC code. One or more entries of standard deviation
and average from the list are selected (202) to correspond to the
SIC code of the Business Entity.
The database 14" provides information about the Business Entity.
Specifically, the database 14" provides records 204, including
rent of the real estate, the SIC code of the Business Entity, and
the number of employees of the real estate. This information is
processed (block 206) to calculate the rent per employee at the
real estate.
The information processed from database 24" and database 14"
is combined (block 208) to calculate the variance. The variance
is compared (block 210) to determine whether to assign a high (block
212), medium (block 214), or low (block 216) indicator value "E"
to the amount. This value "E" is input and further processed
as indicated in the several figures below.
FIG. 8 represents a process flow chart for determining the price
indicator for a use corresponding to rent per sales. As above, a
business database 24" provides records (block 218) including
an SIC code, rent and number of employees. This information is processed
(block 220) to calculate the rent per sales for each record, sorted
(block 222) by SIC code, and processed (block 224) to calculate
the standard deviation and average of the records. Thereafter, a
list is generated (block 226) to provide an average and standard
deviation for each SIC code. One or more entries of standard deviation
and average from the list are selected (block 228) to correspond
to the SIC code of the Business Entity.
The database 14" likewise provides information about the Business
Entity. Specifically, the database 14" provides records (block
230), including rent of the real estate, the SIC code of the Business
Entity, and the sales of the Business Entity in the real estate.
This information is processed (block 232) to calculate the rent
per sales at the real estate.
The information processed from database 24" and database 14"
is combined (block 234) to calculate the variance. The variance
is compared (block 236) to determine whether to assign a high (block
238), medium (block 240), or low (block 242) indicator value "F"
to the amount. This value "F" is input and further processed
as indicated in the several figures below.
FIG. 9 represents a process flow chart for determining the price
indicator for a use corresponding to rent per square foot in a submarket.
A business submarket database 24a" provides records which are
processed (block 244) to calculate the rent per square foot by property
type and submarket. Thereafter, a list is generated (block 246)
to provide an average rent per square foot by submarket and property
type. One or more of the entries from the list are selected (block
248) to correspond to the Business Entity, as described below.
The input "C" from FIG. 6 provides information about
the Business Entity relating to rent per square foot. "C"
is input into FIG. 9 (block 249) to identify property type by SIC
code (block 250), and to identify the submarket by address (block
252).
The information processed from database 24a" and input "C"
is combined and compared (block 254) to determine whether to assign
a high (block 256), medium (block 258), or low (block 260) indicator
value "G" to the amount. This value "G" is input
and further processed as indicated in the several figures below.
FIG. 10 represents a process flow chart for determining the total
price indicator from the uses illustrated in the process flow charts
of FIGS. 6-9. The value "D," from FIG. 6, is input (block
262), weighted (block 264) to produce a weighted indicator (block
266) that is added to similarly processed "E," "F,"
and "G" inputs, from FIGS. 7-9. "E" is input
(block 268), weighted (block 270) to produce a weighted indicator
(block 272) based upon rent per employee. "F" is input
(block 274), weighted (block 276) to produce a weighted indicator
(block 278) based upon rent per sales. "G" is input (block
280), weighted (block 282) to produce a weighted indicator (block
284) based upon rent per sales vs. submarket score. The four weighted
values of blocks 266, 272, 278, and 284 are combined to produce
the total price indicator (block 286). This total price indicator
is output and denoted as "P2."
FIG. 11 represents a process flow chart for determining a grade
indicator for building class. A business database 24" provides
records (block 290), including business address and SIC code. A
submarket database 24b" provides records (block 292, including
building address and class information. The records (blocks 290,
292) are matched (block 294) so that business address is matched
with building address and class; and those matched records are thereafter
sorted by SIC code (block 296) to calculate (block 298) a mode/average
building class by SIC code. A list of mode/average building class
by SIC code is then generated (block 300) and compared (block 302)
with data records (block 304), including building class of the real
estate, from the Business Entity data record (block 306). A variance
is then calculated (block 308) to determine whether to assign a
high indicator (block 310), medium indicator (block 312), or low
indicator (block 314). One of the indicators is produced as an output
grade indicator "H."
FIG. 12 represents a process flow chart for determining a grade
indicator for building age. As above, a business database 24"
provides records (block 316), including business address and SIC
code. A submarket database 24b" provides records (block 318),
including building address and age information. The records (blocks
316, 318) are matched (block 320) so that business address is matched
with building address and age; and those matched records are thereafter
sorted (block 322) to calculate (block 324) an average age and standard
deviation by SIC code. A list of average, standard deviation and
age class by SIC code is then generated (block 326) and compared
(block 328) with data records (block 330), including building age
of the selected real estate, from the business entity data record
(block 332). A variance is then calculated (block 334) to determine
whether to assign a high indicator (block 336, medium indicator
(block 338), or low indicator (block 340). One of the indicators
is produced as an output grade indicator "I."
A total grade indicator is determined as in FIG. 13. "H"
and "I" values from FIGS. 10 and 11 are input, respectively,
as building class and building age records (blocks 342, 344). These
records are weighted, respectively in blocks 346, 348, to determine
a weighted grade-class indicator (block 350) and weighted grade
age indicator (block 352); and these weighted indicators are thereafter
summed (block 354) to produce a total grade indicator "P3."
FIG. 14 shows process flow methodology for determining an area
indicator based upon submarket ratings. A submarket database 24c"
provides records (block 356) of submarket type and rent per square
foot, records (block 358) of submarket type vacancy rate, and records
(block 360) of submarket type absorption. Each of the records (blocks
356, 358 and 360) are sorted, respectively at blocks 362, 364 and
366, to compile records by property type and rent per square foot,
type and vacancy rate, and property type and absorption rate. These
records are ranked, respectively in blocks 368, 370 and 372 to rank
submarkets by (i) rent per square foot for each property type, by
(ii) vacancy rate for each property type, and by (iii) absorption
rates for each property type. Lists are thereafter formed; (blocks
374, 376 and 378), to list submarkets with rank for each property
type. Submarkets are thereafter given indicator values (block 380)
by adding rankings to generate a list of submarkets with indicators
(block 382). The submarket rating from FIG. 14 is generated as an
output "J," which is combined and processed further as
described below. submarket database 24d" to determine the Business
Entity's market and submarket per type (block 388). This is combined
with the information processed from the "J" input to extract
a Business Entity submarket/type rating (block 390) and submarket
ratings for the Business Entity's market (block 392). The Business
Entity's submarket ratings is then compared (block 394) with the
market's median to determine a variance (block 396). From the variance,
a high (block 398), medium (block 400), and low (block 402) indicator
is assigned to determine the area indicator (block 404). An output
"P4" represents the area indicator, as shown and processed
in further process flow diagrams below.
FIG. 16 shows the process flow methodology, according to the invention,
for determining Financial Risk. A business database 24f provides
records of the tenancy status of the business entities, from which
code and tenancy status are extracted at step 406. This information
is processed in step 408 to calculate majority tenancy status for
each SIC code; and listed in step 410. Certain of the records in
the list are extracted to correspond to the Business Entity's SIC
code. Information records representing tenancy status and encumberances
are extracted in step 412 from the Business Entity's data records
24g. The extracted records are used to determine the Business Entity's
tenancy status in step 414 relative to the majority tenancy status
for business entities with a similar SIC code in step 416. This
comparison, together with data pertaining to the presence of encumberances
in step 418, is used to determine whether the Financial risk indicator
is high, in step 422, or low, in step 420. A total financial risk
indicator "K" is utilized and processed in the further
figures detailed below.
A risk indicator for asbestos is generated in the process flow
of FIG. 17. Data 434a, 434b from the Business Entity is used to
extract information about (i) the real estate's location and age
(block 436), and (ii) the real estate's location and building age
(block 438). Such information is processed (block 440) to facilitate
an evaluation about asbestos risk (block 442); and a high asbestos
risk indicator (block 444) or a low asbestos risk indicator (block
446) is assigned based upon that evaluation. An asbestos risk indicator
"L" is then generated for later use.
A risk indicator for sites identified by the Environmental Protection
Agency (EPA) is determined in the process flow shown in FIG. 18.
Data records (block 450) from the Business
A risk indicator for sites identified by the Environmental Protection
Agency (EPA) is determined in the process flow shown in FIG. 18.
Data records (block 450) from the Business Entity are used to extract
(block 452) location information. Data information including a list
of EPA sites (block 454) is compared (block 456) with the extracted
information from the Business Entity to evaluate (block 458) whether
there is a risk associated with the EPA sites. A high risk indicator
(block 460) is assigned if there is a risk; and a low risk indicator
(block 462) is assigned if there is not a significant risk associated
with the real estate location. A risk indicator "M" is
generated for later use.
A risk indicator for radon sites is determined in the process flow
shown in FIG. 19. Data records (block 464) from the Business Entity
are used to extract (block 466) location information. Data information
including a list of hazardous site locations (block 468) is compared
(block 470) with the extracted information from the Business Entity
to evaluate (block 472) whether there is a risk associated with
the hazardous sites. A high risk indicator (block 474) is assigned
if there is a risk; and a low risk indicator (block 476) is assigned
if there is not a significant risk associated with the real estate
location. A risk indicator "N" is generated for later
use.
FIG. 20 shows process flow methodology for combining the several
risk indicators into a total risk indicator P5. Specifically, indicators
"K," "L," "M," and "N" are
entered into blocks 480, 482, 484 and 486; and thereafter weighted
(blocks 488, 490, 492 and 494) to produce weighted indicators (blocks
496, 498, 500 and 502). If available, other financial risks (block
504) can be factored in by weighting (block 506) to produce an additional
weighted risk factor (block 508). The various weighted indicators
(blocks 496, 498, 500, 502 and 508) are combined (block 510) to
generate a total risk indicator P5.
Process methodology for combining the indicators P1-P5 is shown
in FIG. 21. Each indicator P1-P5 is entered into blocks 512, 514,
516, 518 and 520, and weighted by blocks 522, 524, 526, 528 and
530 to generate weighted indicator values (blocks 532, 534, 536,
538 and 540). The values are summed (block 542) to generate a total
score which provides an evaluation of real estate condition for
the Business Entity.
The invention also preferably provides a full BUSINESS REAL ESTATE
REPORT as an information tool that enables top management of any
company that occupies real estate to evaluate in basic terms its
real estate condition relative to other companies in its industry
and the conditions of the real estate market in its area. The report
combines reporting of basic facts about the Business Entity and
its real estate submarket with analytical, comparative data in six
sections: IDENTIFICATION DATA, THE SCORE, REAL ESTATE DATA, KEY
REAL ESTATE RATIOS, MARKET CONDITIONS, FACILITY RELOCATIONS AND/OR
TRANSACTIONS. A list of REAL ESTATE TERMS AND DEFINITIONS is also
provided to assist the Customer that is not a real estate specialist.
The elements of each section are listed and described below:
IDENTIFICATION DATA
The IDENTIFICATION Section of the Business Real Estate Report is
used to communicate basic facts about the Business Entity, including
name, address, contact person and top level financial information.
The section is designed to enable the Customer to immediately identify
the location covered by the report. Some of the identifying details
are listed below.
NAME--The recorded name of the Business Entity, the corporate name
or the DBA name may appear at the user's option; ID NUMBER--An identifying
number that is unique to each company; STREET--The street address
for the location described in the report; CITY/STATE/ZIP--The City,
State and Zip Code for the location described in the report; CONTACT--The
name and title of the principal, employee or agent of the Business
Entity; BILLING INFORMATION--Financial and/or credit information
about the Business Entity and/or the Customer as appropriate; TELEPHONE--The
telephone and fax numbers for the Business Entity's principal, employee
or agent ordering the report; SIC CODE--The Standard Industrial
Classification code assigned by the U.S. Department of Commerce
that describes the primary business of the Business Entity at the
specified location; DATE PRINTED--The date the real estate information
report was printed (this is an automatic "tag" that is
always updated whenever the report is printed so that Customers
of multiple copies will always be able to identify the most recent
report); STATEMENT DATE--The date of relevance for the information
included in the report (typically this will be the balance sheet
and income statement date for financial information; and this date
changes only when the Business Entity's record has been updated
for new financial/real estate information; TOTAL EMPLOYEES--The
total number of staff employed by the Business Entity at all locations
as of the statement date, including locations not covered by the
report. This information is provided to scale the size of the Business
Entity; EMPLOYEES/THIS LOCATION--The total number of staff employed
by the Business Entity at the specific location covered by the report
as of the statement date; TOTAL LOCATIONS--The total number of locations/facilities
operated by the Business Entity(this information includes all facilities
owned or leased by the Business Entity for operations, but does
not include facilities owned strictly for investment purposes, or
leases that have been sublet, and that are not occupied by the Business
Entity); RENT/TOTAL LOCATIONS--The total annual rent in U.S. dollars
paid by the tenant for all locations counted in the "Total
Locations" element; TOTAL SALES--The total amount of annual
revenue/sales earned by the Business Entity's operations at the
specific location covered by the report, as of the statement date
(this element does not include revenue/sales earned by operations
at other locations); GROSS PROFIT--The total amount of annual gross
profit earned by the Business Entity's operations at the specific
location covered by the report, as of the statement date (this element
does not include gross profit earned by operations at other locations);
NET PROFIT--The total amount of annual net profit earned by the
Business Entity's operations at the specific location covered by
the report, as of the statement date (this element does not include
net profit earned by operations at other locations).
SCORE DATA
The Score data section provides an index of the Business Entity's
real estate to provide a quick measures of its condition. The Score
preferably has the five indicators described above: AMOUNT, PRICE,
GRADE, AREA and RISK. The Business Entity's score for each of these
five indicators is provided in this section of the Business Real
Estate report. The aggregate, or total, score is also presented
here.
REAL ESTATE DATA
The REAL ESTATE DATA section of the Business Real Estate Report
provides key facts about the Business Entity's real estate situation
at the specified location. This section does not include any analytical
ratios or comparative data. Such data includes the following:
MARKET--The name of the submarket the Customer site is located
(the submarket is determined by locating the address on a predefined
submarket locations list); SPACE TYPE--The type of space the Business
Entity occupies (this field can be supplied by the Business Entity
or determined by cross-reference from SIC codes); STATUS--A one-word
statement of the Business Entity's occupancy status at the specified
location ("Owns" indicates that the Business Entity has
title and ownership rights to the specified property; "Lease"
indicates that the Business Entity does not have ownership of the
property, but has occupancy rights derived from periodic payments
directly or indirectly to the owner of the property); SQUARE FEET--A
numeric statement of the total square feet of space occupied by
the Business Entity at the specified location (the amount should
included the amount of square feet for which the Business Entity
has exclusive rights, including occupied, "core" and unused
space; and this amount does not include space for which the Business
Entity does not have exclusive rights--e.g. subleased space); RENT/THIS
LOCATION--The annual rent the Business Entity pays in rent for the
right to occupy the square footage in the specified location (this
field does not apply to owned locations); LEASE EXPIRATION--The
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