Predicting real estate prices from the urban environment

Predicting real estate prices from the urban environment

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Marco De Nadai

October 04, 2018
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  1. 1.

    The economic value of neighborhoods: Predicting real estate prices from

    the urban environment MARCO DE NADAI and BRUNO LEPRI
  2. 14.

    The data 14 One year of listed properties in Immobiliare.it

    • 8 biggest Italian cities • 70,000 houses
  3. 15.

    The property: textual features Textual features about the listed property

    • Number of rooms • Square meters • Energy compliance • Garden (yes/no) • etc 15
  4. 17.

    The neighborhood The fundamental geographical unit for: • individuals’ activities

    • social interactions Studied in urban planning, social science, criminology Usually defined through non-overlapping administrative boundaries (census) 17
  5. 20.

    Neighborhood: walkability Walkable and pleasant neighborhoods have a direct impact

    on housing prices Online markets (e.g. Zillow) now show near restaurants and schools 20 Cortright, Joe. "Walking the walk: How walkability raises home values in US cities." (2009).
  6. 21.

    Neighborhood: walkability Walking distance to amenities (~ to Walkscore™) •

    Restaurants and bars (10) • Shops (5) • Schools • Grocery stores • Entertainment • Parks (3) * (n) = depth of choices 21
  7. 23.

    Neighborhood: walkability Walking distance to amenities (~ to Walkscore™) We

    have a score of walkability for each house 23 0 500 1000 1500 2000 Distance 0.0 0.2 0.4 0.6 0.8 1.0 Walkability score
  8. 24.

    Neighborhood: urban theory Jane Jacobs has been one of the

    most influential people in urban planning Diversity => Urban vitality => Higher Housing price? 24 Jacobs, Jane. The death and life of great American cities. Vintage, 1961
  9. 25.

    Neighborhood: urban theory Four essential conditions: 1. Land use mix

    25 Jacobs, Jane. The death and life of great American cities. Vintage, 1961 Land use mix
  10. 26.

    Neighborhood: urban theory Four essential conditions: 1. Land use mix

    2. Small blocks 26 Jacobs, Jane. The death and life of great American cities. Vintage, 1961 Small blocks
  11. 27.

    Neighborhood: urban theory Four essential conditions: 1. Land use mix

    2. Small blocks 3. Aged buildings 27 Jacobs, Jane. The death and life of great American cities. Vintage, 1961 Aged buildings
  12. 28.

    Neighborhood: urban theory Four essential conditions: 1. Land use mix

    2. Small blocks 3. Aged buildings 4. Density 28 Jacobs, Jane. The death and life of great American cities. Vintage, 1961 Density
  13. 29.

    Neighborhood: urban theory Four essential conditions: 1. Land use mix

    2. Small blocks 3. Aged buildings 4. Density 29 De Nadai, Marco, et al. "The death and life of great Italian cities: a mobile phone data perspective." WWW, 2016. OpenStreetMap + census!
  14. 30.

    Neighborhood: cultural capital Cultural capital influences housing prices and people

    behavior • Publishing • Video, radio, photography • Architecture • … And heavy industries (negative!) 30 Hristova, Desislava, Luca M. Aiello, and Daniele Quercia. "The New Urban Success: How Culture Pays." Frontiers in Physics 6 (2018): 27.
  15. 32.

    Neighborhood: security perception 32 Salesses, Philip, Katja Schechtner, and César

    A. Hidalgo. "The collaborative image of the city: mapping the inequality of urban perception." PloS one 8.7 (2013)
  16. 33.

    • Learning safety perception, learned in Milan, Rome • Predict

    on our cities Neighborhood: security perception 33 De Nadai, Marco, et al. "Are safer looking neighborhoods more lively?: A multimodal investigation into urban life." ACM MM 2016.
  17. 34.

    Neighborhood and place: together! 34 PLACE Characteristics of the census

    cell NEIGHBORHOOD Objective and subjective characteristics HOUSE Characteristics of the house
  18. 35.
  19. 36.

    XGBoost 36 = ( + ) Neighborhood features (e.g. walkability)

    Property features (e.g. square feets) Housing price (ground truth) K-fold Cross-validation (with care!) Contiguity matrix
  20. 38.

    38 Results (errors) Model MAE MdAPE Property 148, 109 23,76%

    Property + Neighborhood 104,586 15,44% THE NEIGHBORHOOD SHAPES PROPERTY PRICE BY ~60%!
  21. 40.

    40 How can we explain predictions? From a prediction we

    can go back and compute how each feature contributed to the prediction J: set of trees K: set of features
  22. 43.

    43 Reproducibility Some of the principal competitors: • Make use

    of timeless, private, data • Do not share data • Do not share code
  23. 44.

    44 Reproducibility: Open model + code We created an “Open”

    model: • Make use only of Open data • Share the data • Share the code! https://github.com/denadai2/real-estate- neighborhood-prediction
  24. 45.

    45 Results (errors) Model MAE MdAPE Property 148, 109 23,76%

    Property + Neighborhood 104, 586 15,44% Property + Neighborhood (Open) 138, 929 18.02% GREAT!
  25. 46.
  26. 47.

    Why is it important 47 • We can better understand

    the market • Planning: no oasis in the desert • The surroundings and vitality is important • Responsive predictions without historical data • Gentrification?