Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


Presentation on theme: "Good Morning Dr. Michael Furick –Faculty member at Georgia Gwinnett College, School of Business –Teach Management Information Systems and Marketing."— Presentation transcript:

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

2 Today’s Topic Using neural networks to develop decision support systems to chose tenants for apartment rental. Results of a pilot study

3 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

4 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

5  Picking “good” tenants is vital to rental business success  and sanity

6 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

7 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

8 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

9 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% below to to to to to to 799 over 800 FICO Score Range Rate of Delinquencies

10 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)

11 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)

12 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

13 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)

14 Correlation examined: credit score vs. tenant performance  Data collected One apartment complex 200 tenants that moved in during scores collected on each tenant Tenant performance followed in satisfying lease over 12 months  Traditional statistical methods used to examine correlation

15 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

16 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

17 What would be predictive in Part 2?  Hints from the decision process used in the apartment rental industry

18 Picking tenants more complex than picking customers  Financial consideration  Non-financial considerations  Non financial consideration affected by Fair Housing Laws

19 Decision process mostly manual with a range of data and big dose of “gut feel” 96 units BaltimoreReject if landlord problems or criminal Reject if bankruptcy 395 units ChicagoCredit score in top 15% Reject if landlord problems or criminal 264 units ChattanoogaReject 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.CReject if landlord problem or criminal Reject if bankruptcy

20 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

21 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

22 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

23 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

24 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)

25 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

26 Layers of a Neural Network Input Layer Hidden Layer Output Layer

27 Why use Palisade’s NeuralTools®  Over 50 NN software packages  Evaluated about a dozen  Feature, function, benefit

28 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

29 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 income 3. Driving record of applicant

30 Comments on driving record as predictor in apartment rentals  Auto Industry Credit Performanc e Driving Record Predicts  This Study Credit Performance Driving Record Predicts

31 Limitations with this pilot NN study  Small data set  Single geographic region (one apartment complex)  Data set of those who moved in (sample selection)

32 Next Step for researchers  Proposal submitted to National Science Foundation to fund expansion of the study to the Southeastern U.S.

33 Thanks to Palisade Corporation for hosting the conference

34 Thank you for attending Questions now and later Dr. Michael Furick

35  Copies of the detailed result and model are available for purchase from   Document UMI number:  Citation: Using neural networks to develop a new model to screen applicants for apartment rentals. Furick, Michael T., PhD


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

Similar presentations


Ads by Google