Students for Urban Data Systems

at Carnegie Mellon University

The Affordable Housing Problem and Investor Behavior in Pittsburgh

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By Nick Kharas, Claire Jacquillat, Erin Yanacek, and Maksim Khaitovich

Our cross-functional team from Heinz College and Tepper is interested in looking at the affordable housing scene in Pittsburgh. This was part of a case competition we won, jointly organized by SUDS and the Data Analytics clubs at Heinz and Tepper. Eventually, the topic grew on us as we shared similar experiences while searching for a house to rent or buy. Currently, Pittsburgh has an affordable housing deficit of 17,000 units. Affordability is a reflection of the price of the house, its condition, and the livability of the surrounding area. Thus, solutions to Pittsburgh’s housing challenges need to focus on healthy community development. Keeping that in mind, we propose an “Assess and Address” framework that identifies investor behavior and creates a system of incentives and penalties to address negative influences.

Hazelwood – Our Pilot

Like many other neighborhoods in Pittsburgh, Hazelwood has been hit hard since the city’s steel mills shut down. Still, it has maintained its tight community spirit. New developments like technology company investments, startups and malls are reviving communities in neighborhoods like East Liberty and Lawrenceville, but at the risk of gentrification. Hazelwood is representative of what has been happening across Pittsburgh. Recent developments like Almono and Summerset at Frick Park have the potential to revive the neighborhood. At the same time, we must also ensure that these new developments bring positive change and not displacement for longtime residents.

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Hazelwood’s community focus is still intact, but the recent developments could attract bad investors. As we see from the comparison of average property prices, Hazelwood does not have cases where properties in poor or unsound condition are sold at inflated values, unlike the rest of Allegheny County. While this is a good sign for Hazelwood, it still has some vacant properties and many houses in average or fair condition, which could potentially attract attention from bad investors in the future, if they aren’t already there.

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Note: The graph highlights outliers indicating that some properties in poor or average condition are sold for significantly inflated values. The edges of each box in the plot indicate the interquartile range of sale values for each property condition. The flat line within each box is the median. The dotted lines are the outliers. For our assessment, we considered all valid sales from the property assessments data maintained by the WPRDC. Also, we used zip code 15207 to distinguish Hazelwood from the rest of Allegheny County. Although 15207 covers parts of Glen Hazel and Greenfield, it represents the challenges we aim to address.

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Investor Behavior – Who Are These “Bad” Investors?

A neighborhood stands to benefit if houses in poor or unlivable conditions are purchased and redeveloped. However, some investors are only interested in buying and reselling houses to make a quick profit, without improving their condition or spending on maintenance. Unlike “rehabbers”, “flippers” and “milkers” buy and keep properties in distressed conditions, and hope to sell them off for a profit as quickly as possible. Such behavior does not attract the healthy investors, and also negatively affects the community and quality of life in the neighborhood.

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We decided to look at data driven ways to identify and predict bad behavior using the property assessments data maintained by the WPRDC. Although we do not have any information on house owners, our cognitive solution identifies and flags potentially bad investments and highlights insightful characteristics.

The data does not have class labels that identify bad investments. We ran a k-means clustering algorithm on ownership duration, property value appreciation, and sale price to set our class labels for good and bad investments needed for analytical modeling. We avoided using general definitions for flippers to determine class boundaries as that could ignore any hidden patterns and add bias. For example, any house resold within 12 months for less than $100,000 is potentially a transaction conducted by a flipper. However, using this information alone to set class boundaries ignores several transactions where the property was held for only a little more than a year. The results from clustering directed us to target parcels that were

  • Owned for less than three years, and,
  • The property value depreciated, or appreciated less than 15%, or was sold for less than $90,000.

Any parcels that met the above condition were flagged as properties owned by potentially bad investors, while the rest were labeled as unsuspicious.

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6Once we were able to set the labels for good and bad investments, we ran few classification algorithms to predict investor behavior. We excluded 20% of the data for testing, and trained the remaining 80% data on different classifiers like Gradient Boosting, Random Forest, and Conditional Inference Trees (a modeling technique based on unbiased recursive partitioning). All returned an AUC between 0.70-0.72. Gradient Boosting and Random Forest with feature selection returned marginal improvements, but not significant.

 

Our primary challenge was to not let our results get affected by any bias in the data. A majority of the records are not classified as bad investments, and around 90% of the parcels are not red flagged. This makes sense, as we cannot expect the property market to be completely overrun by flippers. Additionally, the assessments data has missing values for some fields, including those which determine our class labels. For example, some parcels do not have previous sale records, making it difficult to determine how long the house was held before it was sold. We cannot assume that this is missing data, as the property may not have changed ownership more than once in its lifetime. Further, the assessments data set gives us the parcel characteristics as of now, and not as when the property was last sold. For example, the condition of a property may have either improved or deteriorated after it was last sold four years ago, but we have no way to find out. For this reason, the simpler models did not perform well. Logistic regression proved to be computationally expensive with a large number of categorical variables, and decision trees performed poorly on test data.

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We have shared our detailed analysis on GitHub.

 

Our Proposal – Assess and Address

A city like Pittsburgh would like to identify and avoid bad investor activity. However, in an effort to maintain housing affordability, the city cannot drive away potentially good investments that can develop and enrich its vibrant community. To maintain this balance, we propose an Assess and Address framework that gives actionable recommendations.

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Assess

Confirm Results with In-Person Observation

Our analytical model can highlight and flag properties that are potentially at a risk of being owned by flippers. The city inspectors and community leaders can export a list of such at-risk targets, look up information on their owners or landlords, and monitor their behavior.

Address

Sanction Bad Practices

Install practices and systems that would discourage landlords from mistreating their tenants and violating requirements for minimum standards. Our suggestions include:

  • Minimum Property Standards established by the Allegheny County Health Department for rental properties.
  • Rental Registration – Force landlords to act responsibly towards tenants. A good example is the Probationary Rental Occupancy Permit (PROP) set by the city of Raleigh, NC, which aims to ensure better housing quality for tenants and discourages landlords to violate City Codes.

Foster Positive Practices

These are programs that would encourage good investors and community homeowners to invest in enriching and developing the city’s community spirit. Some suggestions and examples of their implementation include:

 

We hope to take this initiative forward with the help of SUDS at Carnegie Mellon University and present our findings to the city council at Pittsburgh.

 

Nick Kharas graduated from Carnegie Mellon University with a Masters degree concentrating in Data Analytics and Business Intelligence. Prior to his time at CMU, he was a business intelligence and data warehousing SME at a Japanese multinational financial holding company. When not a data buff, he enjoys travel, sport and meeting new people. Click here to check out his work on GitHub. You can also connect with Nick at https://www.linkedin.com/in/nickkharas.

Claire Jacquillat is an MBA candidate at Carnegie Mellon’s Tepper School of Business. She focuses her studies on Operations management and Operations research. She is the president of Tepper Data Analytics Club where she strive to foster an integrated use of business analytics in various industries. Before starting her MBA at the Tepper School, she worked as a strategist in Sales Enablement for a Fortune 500 company. You can connect with Claire at https://www.linkedin.com/in/clairejacquillat/en

Erin Yanacek is an MBA candidate at Carnegie Mellon’s Tepper School of Business. In summer 2017, Erin will join McKinsey as a Summer Associate. Prior to business school, Erin was a classical musician. She founded a non profit organization, the Chamber Orchestra of Pittsburgh, and toured internationally performing and teaching classical trumpet. You can connect with Erin at https://goo.gl/BEWz1L

Maksim Khaitovich is an MBA candidate at Carnegie Mellon’s Tepper School of Business. In summer 2017, Maksim will join A. T. Kearney as Summer Data Science Associate. Prior to business school Maksim worked as an engineer and IT consultant in fintech and wireless communications. You can connect with Maksim at https://www.linkedin.com/in/maksim-khaitovich-828a2b47/

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