Good Morning Dr. Michael Furick –Faculty member at Georgia Gwinnett College, School of Business –Teach Management Information Systems and Marketing.

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 earthworry

No tenant………….empty unitworry and lose money

Fear of

this

Causes 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

87%

71%

51%

31%

15%5% 2% 1%

0%

20%

40%

60%

80%

100%

below499

500 to549

550 to599

600 to649

650 to699

700 to749

750 to799

over 800

FICO Score Range

Rate of Delinquencies

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

Derogatory credit in 24 months FICO Installment Loan Score

Repay short term loans auto etc. FICO Finance Score

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 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. Percent satisfactory accounts on credit report

2. Total applicant income3. Driving record of applicant

Comments on driving record as predictor in apartment rentals

Auto Industry

Credit Performanc

eDriving Record

Predicts

This Study

Credit Performance

Driving Record

Predicts

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.