Huang, Yunlong; (2024) Housing Economics and Policy Evaluation in the UK — New Insights from Big Data. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Employing big data techniques, I develop several computer programs to construct large micro-level datasets on residential transactions in England, and demonstrate a solution for standardised and comprehensive data collection methods and frameworks, enabling robust analysis and understanding of housing markets. These novel datasets amalgamate information from various open sources. The smallest dataset encompasses information on one-third of the population’s residential transactions, while the largest dataset covers over 92% of all transactions recorded by the Land Registry. To the best of my knowledge, these are among the most comprehensive datasets utilised in similar studies, offering unique insights into two aspects of the UK residential market. The relationship between transaction price (TP) and time on the market (TOM) remains a longstanding puzzle in the field. Despite extensive research into both the effect of TOM on price and the effect of price on TOM, numerous inconsistent findings persist. In Chapter 3, I address two key issues contributing to these inconsistencies: (i) the omission of controlling for overpricing and (ii) the endogeneity arising from the simultaneous relationship between TOM and price. To tackle these issues, I propose a new overpricing measurement and utilise a two-stage least squares (2SLS) method, employing two novel instrumental variables (IVs): the price revision duration and the council tax band (CTB) for TOM and transaction price, respectively. My results reveal a positive and robust relationship between price and TOM, in line with search theory. Furthermore, my results suggest that chain-free sellers, who are not subject to the constraints of selling their current property to proceed with their next steps, set lower initial asking prices and agree to lower transaction prices, all else being equal, which is associated with agency costs. Using the 2020 Stamp Duty holiday (SDH) in the UK as a quasi-natural experiment, I provide a comprehensive analysis of the tax reduction’s effects on transaction and listing volumes, prices, and market liquidity. Theoretically, I develop a Nash bargaining model and demonstrate that the SDH leads to an increase in prices and a greater surplus for sellers. Empirically, I adopt difference-in-differences (DiD) models and find that the SDH resulted in a 53% increase in housing transactions and an average increase of over 2% in transaction prices; additionally, sellers’ bargaining power strengthened as the SDH deadline approached. Most of the tax savings from the SDH were passed on to sellers in the form of increased prices, leading to reduced affordability for first-time buyers and home movers replacing their main residence. I also discover evidence that market participants utilised the SDH to relocate away from highly urbanised, polycentric areas during the Covid-19 pandemic. My findings indicate that while an SDH can stimulate market activity during an economic downturn and enable the housing market to adjust to changing conditions rapidly, it may also inadvertently reduce housing affordability.

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