A Behavioural Data Approach Towards Predicting Direct Real Estate Markets in the United Kingdom


Type
Thesis
Change log
Authors
Stevens, Donald Garth 
Abstract

In recent years, modern prediction models have evolved to include behavioural data such as user-generated search query data that capture market sentiment and reach beyond the grasp of established macroeconomic indicators. These applications had considerable success in predicting a wide range of economic phenomena with the assumption that internet interaction behaviour resembles probable offline behaviour. Despite the considerable success of this approach, the existing literature argues for the continuous validation of search query keywords and its probable meaning over time to avoid spurious and biased results. Although recent literature attempted to bridge the keyword validation gap, this line of research is still in its infancy. This thesis sets out to examine the validity of web search intention to serve as a “pure” demand proxy for direct real estate market prediction in the United Kingdom. More specifically, it is directed towards constructing web search indices to explore: (i) the extent to which an individual’s true real estate orientated intentions manifest themselves in their web search behaviour and (ii) the magnitude to which real-time information adds value towards the prediction of illiquid asset classes. In doing so, a conceptual framework is produced, which outlines the logic and importance associated with intention specific web search in the digital age, as well as its relation to real estate demand. The empirical findings suggest that intention specific keyword development might be of little importance for aggregate housing and office market forecasts in the United Kingdom. On the contrary, it seems that the viability of intention specific web search keyword development increases when it is directed at a specific regional market. The overall thesis narrative introduces a new way of thinking about web search in the context of economic demand and draws from a variety of principles and methodologies to establish an avenue from which future research can be conducted.

Description
Date
2017-10-04
Advisors
Fuerst, Franz
Keywords
Web Search, Google Trends, Behavioural Finance, Real Estate, Big Data, Machine Learning
Qualification
Doctor of Philosophy (PhD)
Awarding Institution
University of Cambridge