Housing Market Forecasting using Home Showing Events
Yuanyuan Zha, Susan T. Parker, James J. Foster, Vadim Sokolov

TL;DR
This paper introduces a novel housing demand index derived from home showing events data, enabling granular, short-term forecasting of real estate market demand to assist buyers and sellers in decision-making.
Contribution
It develops a new demand index based on microscopic showing events and employs predictive models to forecast housing demand up to twenty weeks ahead.
Findings
High accuracy in short-term demand forecasting.
Granular insights into housing market dynamics.
Proprietary dataset enables novel analysis.
Abstract
Both buyers and sellers face uncertainty in real estate transactions in about when to time a transaction and at what cost. 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…
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Taxonomy
TopicsHousing Market and Economics
