Big Data and Housing - Data and maps update

Scope

The ESPON SO3 activity Big Data for Territorial Analysis and Housing Dynamics has provided evidence on the housing dynamics in European cities and on the wellbeing of European citizens, in particular related to their housing and living situation. The project developed various methodologies for using existing big data sources and platforms as well as indicators for territorial monitoring and analysis.

Main Objective

The main objective of this service is to update the methodological framework and a selection of the Big Data and Housing indicators developed. The methodologies used will be further developed, mainly in relation to cross-border housing markets dynamic, by using the most recent methodological approaches and by collecting, transforming and harmonising relevant data, maps and indicators. As a follow-up on the case study for Geneva, this will all be applied for five additional cases to investigate in more detail cross-border housing markets

Main outcomes

The main outcome of the project is the assessment of potential benefits in further cross-border cooperation on housing issues in order to reduce negative border effects so as to improve access to housing and the quality of life for border residents. In short, the project

  • developed a conceptualisation of cross-border housing markets based on a thorough literature review from both housing studies and border studies;
  • analysed housing policies, markets and planning policies in relation to housing in 11 European countries, outlining the relationship between spatial planning and different types of housing policies;
  • collected and analysed conventional indicators relevant for housing;
  • developed 11 phyton scripts to web scrap housing data from real estate platforms showing the opportunities as well as limits for a harmonised analysis;
  • developed new cross-border indicators that allow to analyse the price differentials and profitability, as well as affordability from different sides of a border to live at the other side of a border. In other words, the project analysed the affordability with the income from one side of a border to live on the other side of the border in comparison to living in the country where wages are earned;
  • analysed the data and discussed the individual case studies as well as it developed an across case study analysis, thus allowing an identification which border regions in the EU show the highest differences or are integrated the most;
  • analysed the accessibility in the border regions and analysed the interdependency with housing prices.

Key messages

The following findings provide some insight into what this research was able to uncover regarding cross-border housing markets:

  • Regional Inequality – major differences in regional incomes across borders can act as a major impetus toward the establishment and foundation of a cross-border housing market
  • Growing Housing  Unaffordability – across  almost  all  cross-border  housing  markets, households are less able to afford their accommodation expenses
  • Spill over of Negative Externalities – unaffordable housing costs in the core region has knock-on negative effects on housing prices in neighbouring regions
  • Potential for further Cross-Border Collaboration – interregional coordination on housing policy can potentially alleviate many issues stemming from the growth of cross-border housing markets
  • Markets and Social Wellbeing – the housing market is complex and multifaceted, and social wellbeing accounts as an critical factor in a household’s decision to migrate across a border
  • Dependency of  Housing  Markets  on  Fiscal  Policies – activities in the housing markets are linked to interest rates and national fiscal policies, such as subsidies
  • Relevance of  Accessibility – while accessibility measurements did not play a critical role in impacting housing price, cross-border accessibility does allow for the development of cross-border housing markets via permitting larger cross-border movements and flows

Contractors

  • Intelligent Atlas S.L., ES (lead contractor)
  • Mcrit S.L., ES
  • Cambridge University Technical Services Limited, UK

Budget

€ 72.500.00

Lifetime

September 2021 – September 2022

Deliveries

  • Draft Final delivery, 21 April 2022
  • Final delivery, 30 August 2022

Contact: Marjan van Herwijnen (Senior Project Expert) marjan.vanherwijnen@espon.eu and Caroline Clause (Senior Financial Expert) caroline.clause@espon.eu

 

Documents

ESPON_Big data_FR_Main_Report.pdf

  • Acrobat Document | 4.47MB

Annex 1_FR_Technical Report.pdf

  • Acrobat Document | 10.07MB

Annex 2_FR_Case Study_AT-SK.pdf

  • Acrobat Document | 7.22MB

Annex 3_FR_Case Study_DK-SW.pdf

  • Acrobat Document | 6.98MB

Annex 4_FR_Case Study_ NoI-IR.pdf

  • Acrobat Document | 7.75MB

Annex 5_FR_Case Study FR-ES.pdf

  • Acrobat Document | 6.13MB

Annex 6_FR_Case Study_FR-CH.pdf

  • Acrobat Document | 7.91MB

Annex 7_FR_Case Study_RO-BG.pdf

  • Acrobat Document | 3.31MB

Annex 8_FR_Phyton Scripts.pdf

  • Acrobat Document | 525KB

Annex 9_FR_Accessibility.pdf

  • Acrobat Document | 26.48MB