Good Morning

Presentation Transcript

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

Causes this

Fear of 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

100%

87%

80%

71%

Rate of Delinquencies

60%

51%

40%

31%

15%

20%

5%

2%

1%

0%

below

499

500 to

550 to

600 to

650 to

700 to

750 to

over

800

549

599

649

699

749

799

FICO Score Range

Credit score is widely used because it is predictive and easy

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”

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)