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 Cause worry s this No tenant………….empty unit worry and Fear lose money 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% Rate of Delinquencies 80% 71% 60% 51% 40% 31% 20% 15% 5% 2% 1% 0% below 500 to 550 to 600 to 650 to 700 to 750 to over 499 549 599 649 699 749 799 800 FICO Score Range 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 income 3. Driving record of applicant Comments on driving record as predictor in apartment rentals Auto Industry Driving Credit Predicts Record Performance This Study Credit Predicts Performanc Driving e 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.
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