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CiTown - The financialisation of residential housing Amsterdam case study

Explore the financialisation of housing in Amsterdam through an analytical approach, examining housing costs, buyer and seller segmentation, ownership changes, and determinants of housing costs.

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CiTown - The financialisation of residential housing Amsterdam case study

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  1. CiTown - The financialisation of residential housing • Amsterdam case study • EC Joint Research Centre • Territorial Development Unit (JRC.B3) • ricardo.barranco@ec.europa.eu • chris.jacobs-crisioni@ec.europa.eu • City of Amsterdam • City Strategy Team • j.taks@amsterdam.nl • European Week of Regions and Cities • Brussels, 8th October 2019

  2. Contents: The Financialisation of housing: Policy for affordability - City of Amsterdam 2. Modelling Amsterdam housing costs - LUISA Territorial Modelling Platform

  3. 6696 (August, 2019) Number of days available Amsterdam AirBnB Prices (€/night) 19.619 houses for rent on AirBnB 6696 houses rent out long term as hotels: (Frequently and over 60 days/month) = 5% of the owner-occupied properties.

  4. 2. Modelling Amsterdam housing costs: - A Data Science approach to analyse prices • EC Joint Research Centre • Territorial Development Unit (JRC.B3) • ricardo.barranco@ec.europa.eu • chris.jacobs-crisioni@ec.europa.eu • European Week of Regions and Cities • Brussels, 8th October 2019

  5. Amsterdam housing costs: • The following slides show an analytical approach to study Amsterdam’s housing costs at fine resolution. • Is based on 2015 transaction data containing information for10958 houses, several LUISA layers and uses price/m2 as the study dependent variable. • The map gives an overview of Amsterdam’s housing costs spatial distribution. • Price/m2 in “Inner Ring” areais considerably higher. Amsterdam Housing Prices (€/m2): 2015

  6. Amsterdam housing costs: • The following slides show an analytical approach to study Amsterdam’s housing costs at fine resolution. • Is based on 2015 transaction data containing information for10958 houses, several LUISA layers and uses price/m2 as the study dependent variable. • The map gives an overview of Amsterdam’s housing costs spatial distribution. • Older houses aremainly located within the “Inner Ring” area. Amsterdam Construction Year

  7. Correlation Analysis: • For each of the 10958 houses were mapped 31 spatial indicators and then aggregated according to general thematics:Health, Education, Basic Services, Comsumption&Recreation, Transportation • They represented 2 main classes: • Distance indicators (distance to closest point) • Density indicators (total points within 500m) • Calculated correlation coeficients for Price/m2. Correlation Distance analysis Density analysis

  8. Applied to 2 main groups: • Buyers segmentation • Seller segmentation • The 4 used variables were: • Type of residence; Type of buyer/seller; • Number of lots owned; Classification Segmentation Analysis: • Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways. Buyers Segmentation: • Notes: • Often missing information concerning buyers/sellers (*unknown). • Buying residents, invest in smaller areas but pay more per m2. • Companies & Private owners buy bigger areas maybe for renting or office space. Sellers Segmentation:

  9. Amsterdam ownership changes: • Data description: • The points represent 24231 real estates: • Built for residential purposes; • Privately owned by natural persons or regular companies in 2018; • Underwent at least one ownership change between 2015 and 2017. • Methodology: • Analysis of clusters of high and low ownership change from 2007 to 2017 in real estate units in the municipality of Amsterdam. • Notes: • Houses in West and Zuid boroughs located within A10 motorway seem to be associated with recent ownership changes hotspots (red). Clusters of ownership change in privately owned houses: 2007-2017

  10. Modelling housing costs: • The aggregated indicators were applied on a machine learning regression model. The objective was to calculate what are the main housing cost determinants (R2 = 0.56, MAE: 744€/m2). • The Construction Year is the main driver, probably reflecting location/aesthetic preferences. Location plays an important role here also represented by InnerRing. The Buyer Segment is the 3rd main determinant of the price/m2. • Main determinants: • Houses between 1950 and early 2000 are associated with lower output values. The market as a preference for older (pre-1950). Construction Year Output value Output value • The relation between housing costs and distance to centre is not linear. After 4 km it’s when most houses are located as outside the InnerRing (blue dots). Inner Ring Distance to centre Outter Ring Feature Importance (%) Impact on output value

  11. Modelling housing costs: • The aggregated indicators were applied on a machine learning regression model. The objective was to calculate what are the main housing cost determinants (R2 = 0.56, MAE: 744€/m2). • The Construction Year is the main driver, probably reflecting location/aesthetic preferences. Location plays an important role here also represented by InnerRing. The Buyer Segment is the 3rd main determinant of the price/m2. • Main determinants: • Buyer segments influences the price/m2: • Residentsarea linked with higher values per m2. • Private owners/Companies have an opposite dynamics. They are linked with lower price/m2. • Private owners/Companies potentially buy at lower prices for then selling more expensive: e.g. office space and buildings to be split/renovated. This is more evident closer to the city centre (“Inner Ring”). Inner Ring Buyer Segments Output value Outter Ring Resident Resident Company Private owner Feature Importance (%) Impact on output value

  12. Main determinants: • Conclusions: • According to this modelling exercise Amsterdam prices/m2 are mainly driven by the location (Year, InnerRing, Distance to centre), aesthetic (Year) and type of buyer (buyer segment). • This type of advance analysis allows having insights of the main determinants and possible interactions influencing housing prices. • Combines different datasources: • House geolocation; Neighbourhood characteristics; Location/density of services; Buyers/sellers types; Transactions... • The graphical outputs make it intepretable to non-experts aiding decision makers on their policy and comunication to the general public. • Replicable method to compare European cities. • Construction year is associated with higher price/m2 for pre-1950 constructions. Distance centre (km) Construction Year Predicted price (€/m2) Predicted price (€/m2) • Distance to centreafter 4 km impacts negatively the housing prices.

  13. Thank you! Obrigado! Dank u! • EC Joint Research Centre • Territorial Development Unit (JRC.B3) • ricardo.barranco@ec.europa.eu • chris.jacobs-crisioni@ec.europa.eu • City of Amsterdam • City Strategy Team • j.taks@amsterdam.nl • European Week of Regions and Cities • Brussels, 8th October 2019

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