Our research brings together leading researchers, industry experts and public sector stakeholders to provide solutions to the global social challenge of affordable housing.
Lack of affordable housing can have a significant impact on productivity and economic growth worldwide, and may lead to increased segregation and inequality within communities.
To date, in the UK the supply of housing at affordable levels has been approached primarily through the provision of subsidised government schemes such as shared ownership, Help-to-Buy and build-to-rent, in addition to the rising market of the private rented sector.
With restrictive public budgets the provision of affordable housing has meant institutional investors and housing associations taking a more active role in the financing of residential developments.
Our research on affordable housing brings together academics and a wide range of industry stakeholders. We work closely with private sector investors, the public sector and non-profit organisations to enhance our understanding of affordable and social housing.
Through this collaboration between academic research and industry expertise, we aim to explore new models of affordable housing provision and financing, produce relevant policy advice and provide solutions to the global challenge of the housing affordability crisis.
- Spatial Dependence in Apartment Transaction Prices During Boom and Bust, Regional Science and Urban Economics (Jan 2018)
Dongwoo Hyun (University of Reading) Stanimira Milcheva
- Spatio-temporal effects of an urban development announcement and its cancellation on house prices – A quasi-natural experiment, Journal of Housing Economics (Sept 2017)
Dongwoo Hyun (University of Reading), Stanimira Milcheva
- Is Financial Regulation Good or Bad for Real Estate Companies? Journal of Real Estate Finance and Economics (Oct 2017)
Martin Hoesli (University of Geneva) Stanimira Milcheva, Alex Moss (Cass Business School, University of London)
- The Housing Market Channel of Monetary Policy Transmission in the Euro Area, Journal of European Real Estate Research (2016)
Stanimira Milcheva, Steffen Sebastian (University of Regensburg)
- Bank Integration and co-movements across Housing Markets, Journal of Banking and Finance (Nov 2016)
Stanimira Milcheva, Bing Zhu (Henley Business School, University of Reading)
- BSCPM Doctoral conference 2019: affordable housing and big data
In June 2019, the second BSCPM Doctoral Conference took place, organised by PhD students Yunlong Huang and Thomas Weston. The theme this year was affordable housing and big data. With presentations from both academics and PhD students, a wide variety of topics related to housing and machine learning were covered, exploring areas such as affordable housing, migration and demography, and machine learning methodologies.
Download the presentations:
- Understanding London’s Urban Metabolism: Baseline setting, reproducibility [pdf 7.2MB] - Boyana (Bonnie) Buyuklieva
- Strategic facility and asset management - machine learning based methods - Zigeng Fang [pdf 946 KB]
- Is affordable housing, about housing? - Sepehr Zhand [pdf 7.5 MB]
- Big data, machine learning, and econometrics: applications to real esate - Marc Francke [pdf 8.6 MB]
- The Use of Agent-Based Modelling in Modelling Migration - Lois Liao [pdf 1.6MB]
- The Case of Affordable Housing - June 2018 symposium
On 22 June 2018, The Bartlett School of Construction and Project Management hosted a one day symposium - The Case of Affordable Housing: Private Sector Investment in Social Infrastructure - exploring the role of private investment in housing affordability.
The symposium brought together leading academic researchers in public policy, urban planning, real estate and economics, joining representatives from local authorities and government and industry financers to explore alternative solutions to the housing affordability crisis faced by the UK and the role of private sector investment.
A number of School academics, PhD students and honorary and affiliate staff carry out research in this area.
- Dr Stanimira Milcheva
Dr Stani Milcheva's research interests lie in the intersection of housing and finance. Stani’s research is empirical and she uses both micro and macro level data to address questions related to the way housing markets function. She has investigated the linkages between markets as well as individual properties using spatial econometrics. In addition, Stani has explored the role of institutional investors and lenders for housing affordability including the rental market.
Stani is currently working on projects using US and UK data encompassing the rented sector as well as covering schemes such as shared ownership. An important characteristic of her research has been to account for linkages and spillovers of information between assets and markets.
- Professor Jim Meikle
Professor Jim Meikle has a long standing interest in housing and housing economics. He worked on housing and urban development projects in Libya and Egypt in the 1970s and low energy housing design studies in the UK in the 1980s. In the 1990s Jim worked on UK housing association cost limits and house price indices for the UK government, a study of Japanese prefabricated housing for the Construction Industry Research and Information Association (CIRIA) and a study of the construction industry and housing in Russia for the World Bank.
In the 2000s Jim worked on a study of self-build housing for the Joseph Rowntree Foundation, the economics of park homes for the UK government and a number of studies with the Commission for Architecture and the Built Environment (CABE) on housing standards, housing data and housing development. Most recently he has worked with the African Development Bank on informal housing construction in Africa.
- Yunlong Huang
Yunlong Huang has a background in financial modeling, advanced statistics and civil engineering, specialising in applied predictive modeling. His research interests lies in the intersection of data science, machine learning and housing. He is developing a system to collect research data and use specialised computational algorithms to combine with multi-source data. This will result in one of the biggest databases of UK housing market studies. Yunlong utilises modern statistical and machine learning methods, industry grade workflow and best practices while developing quantitative academic research methods.
- Thomas Weston
Tom Weston uses the application of machine learning methodologies to explore various dynamics within the real estate market, such as locational volatility, sentiment analysis of volatility, and the susceptibility of the market to boom and bust phenomena. After completing his BA in Geography at the University of British Columbia he worked for a number of years both in Canada and the UK, in project management and land development. He then completed an MSc in Smart Cities and Urban Analytics at UCL, where he began his research on the application of machine learning to real estate economics and finance.