Waiting to Be Sold: Prediction of Time-Dependent House Selling Probability

Abstract

Buying or selling a house is one of the important decisions in a person's life. Online listing websites like "zillow.com", "trulia.com", and "realtor.com" etc. provide significant and effective assistance during the buy/sell process. However, they fail to supply one important information of a house that is, approximately how long will it take for a house to be sold after it first appears in the listing? This information is equally important for both a potential buyer and the seller. With this information the seller will have an understanding of what she can do to expedite the sale, i.e. reduce the asking price, renovate/remodel some home features, etc. On the other hand, a potential buyer will have an idea of the available time for her to react i.e. to place an offer. In this work, we propose a supervised regression (Cox regression) model inspired by survival analysis to predict the sale probability of a house given historical home sale information within an observation time window. We use real-life housing data collected from "trulia.com" to validate the proposed prediction algorithm and show its superior performance over traditional regression methods. We also show how the sale probability of a house is influenced by the values of basic house features, such as price, size, # of bedrooms, # of bathrooms, and school quality.