Economic Modeling of Apartment Listings Collected from Craigslist
Housing prices remain unique among commonly traded goods and services, in that nearly no geographic competition exists. This feature is often compounded by high costs and low frequencies of transactions. Demonstrating the importance of location in urban centers, is the extreme segregation of central business districts which radiate outward into residential areas. (Lucas and Hansberg, 2002).
Apartment pricing in the residential real estate market remains stubbornly inefficient. With roughly half of US rentals owned by small, “mom and pop” investors, valuation errors are predictably commonplace. Anchoring effect has been demonstrated to play a significant role in price setting. Landlords who buy at a market’s peak systematically charge 2-3% more and sit in inventory 6% longer than counterparts who purchased at the market’s trough (Giacoletti and Parsons. 2022). This inefficiency is at least partially explained by the conventional wisdom of the “1% rule of real estate”, which states rent should equal 1% of the purchase price. Small landlords simply lack the tools necessary for property valuation and pay a high price as a result. These peculiarities and inefficiencies underscore the need for a deeper understanding of price setting.
The formal approach to this problem is known as hedonic modeling which attempts to determine the extent to which each factor affects a property’s price. By analyzing public listings, we can construct a model that accurately explains the bulk of the variation in price between rental units.
Optimizing for hedonism? I’m listening…
The data-gathering step was carried out using Selenium web-browser and Chromedriver for Linux. The scraping module first iterates over each page of listings, generating a series of URLs. The next module iterates over the listing URLs and generates a “Listing” object which stores the attributes of each unique listing. Attributes like “beds” and “baths”, but also “laundry” and “latitude” which are also useful.
The scraped information was stored in a Pandas dataframe for analysis. The variable “descript” is unique because it contains the entire free-text field provided by the listing entity. From this attribute, the fields “hardwood” and “stainless” were generated by simple string matching and then one-hot encoded.
So we have our dataset!
When the data were de-trended by using user-reported square-footage measurements, correlation values could be calculated. Of note, in-unit washer/dryer, air-conditioning and proximity to Chicago’s downtown correlated strongly with higher price-per-square-foot. Landlords reporting no laundry on site report an average price $1100/month lower than those reporting in-unit laundry. Indicators of renovation, ‘stainless’ keyword and listed air-conditioning are also lower in laundry-absent properties. This provides support for the theory that these attributes can be largely considered together as indicators of property investment.
Still unclear though, is the actual hedonic value of adding any specific investment. To determine these marginal values in better detail, a GAM was trained on the dataset. By plotting the curves generated, we see the effects of each variable in isolation, along with their respective 95% confidence intervals.
For the hedonic model, we use PyGam. GAMs use a spline-fit to generate a fit of the data one attribute at a time. These predictions are usually not terribly reliable on their own, but in conjunction, they can be hugely revealing.
So check this out – The addition of air conditioning is valued at $75 to $200 per month. In-unit laundry, we see providing a benefit from $100 to over $300 per month. Updated kitchens (though not on the above figure), lend an additional $125 in value. As an example problem, assume a landlord would like to determine the additional value adding air conditioning would contribute to a rental unit. At time of writing, an 8% APR home-equity loan is typical for well-qualified borrowers. The EPA recommends 20,000 BTUs for the typical 1000 square-foot Chicago apartment. Prices for ductless split units vary widely and can range from as little as $3000 to upwards of $20,000. A washer/dryer set typically costs from $700 to $1800, and a stainless kitchen set (refrigerator, stove, dishwasher, and microwave) ranges from $2,300 to $11,000. This gives us a range of potential budgetary possibilities $6,000 to $32,800. The possible upside benefit is in the range of $225 to $700 per month or $2,700 to $8,400/year.
Through use of the confidence intervals provided by the GAM, we can simply add the mesh grids to generate predictive values for the expected change in rental incomes. The CI95 return on investment is found to range from $280 to $887, with a mean of $498 per month, or $5,976 annually, and $59,760 over the lifetime of the loan.
This result is so convincing, it actually broke the project I was trying to do. I was hoping to get a project that would make me write a simulation problem but nope. We get a discrete answer – put a laundry in your unit.
The key drawback of this study is that while it performs well predicting the mean asking value of a property, it fails to take time-on-market into account. The monthly nature of most rental agreements implies that there is an optimal time-on-market for listings looking to maximize revenue. Listing a property at too low of a price may secure a tenant quickly, but leave money on the table. Listing a property at higher prices corresponds to longer times on-market, but regardless of which scenario, a move-in date will typically remain the same. Landlords should therefore ask exactly as much as they can without excessively risking the loss of a month’s rent. To further investigate this, 90 days or more of rental listing data could be used to track the rate of posting renewal (Craigslist posts are renewed weekly) and attempt to learn from their correlations with this model’s predictions.
Further enriching this model is also possible. With advances in computer vision, it may be possible to train a neural network to perform regression analysis of posting images and thereby provide guidance to an interpretable model such as a GAM.
References
Giacoletti, M., and Parsons, C. A. 2022 Peak-Bust rental spreads. Journal of Financial Economics
Goodman, L and Mayer, C. 2018. Homeownership and the American Dream. The Journal of Economic Perspectives.
Mason, C. and Quigley, J.M. 1996 Non-Parametric Hedonic Housing Prices Housing Studies.
Osterbring, M., Mata, E., Thuvander, L., Wallbaum, H. 2019. Explorative Life-cycle assessment of renovating existing urban housing-stocks. Building and Environment.
Rosen, S. 1974. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy.
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