Good Morning
Dr. Michael Furick
–Faculty member at Georgia Gwinnett
College, School of Business
–Teach Management Information Systems
and Marketing
Today’s Topic
Using neural networks to develop
decision support systems to chose
tenants for apartment rental.
Results of a pilot study
If we asked about rental property…
Owning rental property is the best
financial decision you will ever make
and
Owning rental property is the worst
financial decision you will ever make
Three levels of apartment tenants
Good tenants…….heaven on earth
Bad tenants……….hell on earth
worry
No tenant………….empty unit
worry and
lose money
Fear
of
this
Cause
s this


Picking “good” tenants is vital to
rental business success
and sanity
Many decisions about tenants get
made every year




34 million households live in rental
housing (held steady due to
immigration)
20% renters above $60k income
20% renters below $10k income
56% of rental units owned by
individuals
How do other industries pick
“customers”

Most rely on credit reports and
credit scoring to predict consumer
financial behavior




Banks
Car dealers
Mortgage brokers
etc
What is a credit report and score?


Credit report- a multi page report
that profiles a consumer’s financial
transactions
Credit score- mathematical means
of summarizing the credit report
into a three digit number
Credit score is widely used because it
is predictive and easy
Delinquency Rates by FICO Mortgage Risk Score
Rate of Delinquencies
100%
87%
80%
71%
60%
51%
40%
31%
15%
20%
5%
2%
1%
0%
below
499
500 to
549
550 to
599
600 to
649
650 to
699
700 to
749
FICO Score Range
750 to over
799 800
Success has caused credit score use
to spread to other industries
Auto industry uses credit score to
 Determine who gets auto insurance
 What price to charge for an auto policy
Two studies found that a lower credit score
means
 Up to 50% more accidents
 Bigger claims ($918 vs. $558)
Credit data and credit scores should
work in apartment rentals
Two Part Study
 First part of study looked at tenant
performance vs. six commercially
available credit scores
(statistical analysis)
 Second Part: If credit is not
predictive then what is predictive?
(neural network analysis)
Credit data and credit scores should
work in apartment rentals


First part of study looked at tenant
performance vs. six commercially
available credit scores
22 different credit scores are
available from Experian
Six scores tested from Experian


FICO Mortgage Risk Score
FICO Advanced Risk Score


FICO Installment Loan Score



Repay short term loans auto etc.
FICO Finance Score


Derogatory credit in 24 months
Loans from non-traditional sources
National Risk Score
Sureview Non-Prime score

(non-prime bankcard applicants)
Correlation examined:
credit score vs. tenant performance

Data collected





One apartment complex
200 tenants that moved in during 2002
6 scores collected on each tenant
Tenant performance followed in
satisfying lease over 12 months
Traditional statistical methods used
to examine correlation
Results Part 1: credit data not
predictive of tenant performance


No correlation between credit data
and tenant performance in
satisfying the terms of their lease
R square approaching zero
“We have as much trouble with people with
good credit as we do with people with bad
credit”
property manager quote
Why are commercial scoring models
not predictive in selecting tenants?


??????
Many “good working” models filter
out consumers with



Less job tenure
High ratios of debt to income
Older vehicles
What would be predictive in Part 2?

Hints from the decision process
used in the apartment rental
industry
Picking tenants more complex than
picking customers



Financial consideration
Non-financial considerations
Non financial consideration affected
by Fair Housing Laws
Decision process mostly manual with a
range of data and big dose of “gut feel”
96 units Baltimore
Reject if landlord problems or criminal
Reject if bankruptcy
395 units Chicago
Credit score in top 15%
Reject if landlord problems or criminal
264 units Chattanooga
Reject is landlord problem or criminal
Reject if bankruptcy
210 units Athens, Ga.
Income 3 times monthly rent
80% satisfactory accounts
Reject if landlord problems or criminal
68 units Washington D.C
Reject if landlord problem or criminal
Reject if bankruptcy
Nationally, property managers make
rental decisions on a range of items



33.8% ran criminal backgrounds
62.6% ran credit reports
65.5% called references
Rental Property Reporter



50.6% ran credit reports
52% verified income
75.5% relied on personal interviews
U.S. Census Bureau
Opportunity to standardize decision
making with a Decision Support Model


Data to be a mix of financial and
non-financial items (matching
current decision process)
Apartment managers suggested 76
possible variables
Sample of data elements used in
neural network model

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
7
From out-of-state (Application)
11 Size of employer
(Chamber of Commerce)
12 Number of years with employer
(Application)
15 Income
(Verification)
20 Number of people to occupy apartment
(Application)
34 Type of vehicle one
(Application)
35 Age of vehicle one
(Application)
48 Estimated monthly installment loan payments
(Credit Report)
68 Number of driving infractions
(DMV report)
73 Information found on county criminal search
Data collection process



One apartment complex
Data elements collected on 60
tenants as they moved in during
2004
Tenants lease performance tracked
over 12 months
Why use neural networks to create this
model


Neural network – an artificial
intelligence system that is good at
finding and differentiating patterns
modeled after the brain’s mesh-like
network of interconnected
processing elements (neurons)
Why use neural networks to create this
model

NNs good with unstructured data



how do data elements interact with
each other or with the output
Analyze nonlinear relationships
Learn and adjust to new
circumstances
Layers of a Neural Network
Input Layer
Hidden Layer
Output Layer
Why use Palisade’s NeuralTools®
Over 50 NN software packages
 Evaluated about a dozen
 Feature, function, benefit

Actual model creation details not
covered here



Data divided into test and training
data
Model run several hundred times
using various combinations of
variables
Prediction accuracy recorded and
analysis completed
What did the model find?


Model accurately predicted 69.1%
of tenants (good and bad )
Three data elements became most
important in choosing tenants
1.
2.
3.
Percent satisfactory accounts on credit
report
Total applicant income
Driving record of applicant
Comments on driving record as
predictor in apartment rentals

Auto Industry
Credit
Performance

Predicts
Driving
Record
This Study
Credit
Performanc
e
Predicts
Driving
Record
Limitations with this pilot NN study



Small data set
Single geographic region (one
apartment complex)
Data set of those who moved in
(sample selection)
Next Step for researchers

Proposal submitted to National
Science Foundation to fund
expansion of the study to the
Southeastern U.S.
Thanks to Palisade Corporation for
hosting the conference
Thank you for attending
Questions now and later
Dr. Michael Furick
Copies of the detailed result and model are available
for purchase from
 www.il.proquest.com
 Document UMI number: 3215298
 Citation:
Using neural networks to develop a new model to
screen applicants for apartment rentals. Furick,
Michael T., PhD. 2006.
