Housing Market Forecasting using Home Showing Events

Opportunities to support urban economic decision-making with analytical models are extensive in the real estate market. Both buyers and sellers face uncertainty in real estate transactions in about when to time a transaction and at what cost. In the current real estate market, both buyers and sellers make decisions without knowing the present and future state of the large and dynamic real estate market. Current approaches rely on analysis of historic transactions to price a property. However, as we show in this paper, the transaction data alone cannot be used to forecast demand. We develop a housing demand index based on microscopic home showings events data that can provide decision-making support for buyers and sellers on a very granular time and spatial scale. We use statistical modeling to develop a housing market demand forecast up to twenty weeks using high-volume, high-velocity data on home showings, listing events, and historic sales data. We demonstrate our analysis using data from seven million individual records sourced from a unique, proprietary dataset that has not previously been explored in application to the real estate market. We then employ a series of predictive models to estimate current and forecast future housing demand. A housing demand index provides insight into the level of demand for a home on the market and to what extent current demand represents future expectation. As a result, these indices provide decision-making support into important questions about when to sell or buy, or the elasticity present in the housing demand market, which impact price negotiations, price-taking and price-setting expectations. This forecast is especially valuable because it helps buyers and sellers to know on a granular and timely basis if they should engage in a home transaction or adjust their home price both in current and future states based on our forecasted housing demand index.