Increasing the Value of Search Subscriptions for Housing Market Analyses

Posted: 1 Nov 2017 Last revised: 19 Dec 2018

See all articles by Andy Egger

Andy Egger

Realmatch360

Vanessa Kummer

University of Zurich

Maik Meusel

University of Zurich

Karl Schmedders

University of Zurich

Date Written: October 30, 2017

Abstract

The drastic increase in the number of vacant accommodations in some regions of Switzerland and the simultaneous housing shortage in others are the result of not knowing where people want to live and, therefore, of having built accommodations in the wrong locations. In order to better understand what people are searching for, the Swiss start-up Realmatch360 began to analyze search subscriptions to real estate platforms. Using search subscriptions allows the company to get a better understanding of people’s preferences for housing and even to identify unmet demand. In this paper, we propose powerful approaches based on unsupervised learning to maximize the benefits of using search subscriptions exhibiting many missing entries for housing market analyses.

Keywords: Demand Forecasting, Imputation, Machine Learning, Real Estate

Suggested Citation

Egger, Andy and Kummer, Vanessa and Meusel, Maik and Schmedders, Karl, Increasing the Value of Search Subscriptions for Housing Market Analyses (October 30, 2017). Available at SSRN: https://ssrn.com/abstract=3061979 or http://dx.doi.org/10.2139/ssrn.3061979

Andy Egger

Realmatch360 ( email )

Alte Wollerauerstrasse 53
Wollerau, 8832
Switzerland

Vanessa Kummer (Contact Author)

University of Zurich ( email )

Moussonstrasse 15
Zürich, CH-8044
Switzerland

Maik Meusel

University of Zurich ( email )

Rämistrasse 71
Zürich, CH-8006
Switzerland

Karl Schmedders

University of Zurich ( email )

Moussonstrasse 15
Zürich, CH-8044
Switzerland
+41 (0)44 634 3770 (Phone)

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