Valuing Affordability: Utility and Accuracy Optimisation An Econometric and Data Driven Approach
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Authors
Burke, Mark John
Advisors
Fuerst, Franz
Date
2022-04-22Awarding Institution
University of Cambridge
Qualification
Doctor of Philosophy (PhD)
Type
Thesis
Metadata
Show full item recordCitation
Burke, M. J. (2022). Valuing Affordability: Utility and Accuracy Optimisation An Econometric and Data Driven Approach (Doctoral thesis). https://doi.org/10.17863/CAM.77984
Abstract
The problem of sustainable and affordable housing stock which is accessible to low income
earners is particularly pronounced in developing economies. Housing solutions have the
highest potential for social impact in these economies. Nonetheless, uncertainty exists around
stock development choices in resource constrained environments. Where the provision of
access has failed, issues of land redistribution become topical. The literature review outlines
both current and past work on understanding effective responses to housing needs, considering
both demand and supply side interventions for increasing housing stock. The contribution of
this research is presented in two related themes, the first of which is focussed on combining
house value data with other metrics focussed on social outcomes (including wellbeing and
health) in order to quantify the extent to which there is correlation. Using factor analysis and
instrumental variables, we are able to show that a range of ownership and wellbeing metrics
are improved through subsidy programmes, whereas land grants and land restitution lead to
neutral or adverse outcomes. Equally, measuring social attitudes as it relates to ownership
of properties within estimated value ranges is presented as a novel function for measuring
housing access and its impact. We found that government support in general as well as
support for government handling of housing and land issues is increased by as much as 11%,
when asset scores increase. The second theme is focussed on finding accurate measures of
property preference drivers and its occupancy, and by extension property value drivers, across
geographies. This is achieved using both conventional hedonic regression modelling and
emerging machine learning applications. We use increased accuracies to test the feasibility
of building a more robust model for estimating value. Initial analysis is focussed on a subset
of a comprehensive dataset in a generic setting, while further iterations expand the scope
of datasets subjected to the constructed model to include both new locations as well as a
broader sweep of features. Further work still applies industry leading cloud computing
infrastructure to a supervised learning environment to attain accuracy gains. Likewise, our
stepwise approach to modelling internet usage allowed for up to 12% increases in the per
utilised hour value estimation of house sharing units.
Keywords
Housing, Valuations, Machine learning, Econometrics
Sponsorship
Mandela Rhodes Leverhulme Scholarship
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.77984
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