Predicting real estate prices from the urban environment

Transcript

1. The economic value of neighborhoods:
   Predicting real estate prices from the urban
   environment
   MARCO DE NADAI and BRUNO LEPRI
   

   View Slide

2. 2
   Let’s find a new house
   

   View Slide

3. 3
   Let’s find a new house
   

   View Slide

4. 4
   Let’s find a new house
   

   View Slide

5. 5
   Let’s find a new house
   

   View Slide

6. 6
   Let’s find a new house
   

   View Slide

7. 7
   Let’s find a new house
   

   View Slide

8. 8
   Let’s find a new house
   

   View Slide

9. 9
   Let’s find a new house
   

   View Slide

10. 10
    Let’s find a new house
    

    View Slide

11. 11
    Let’s find a new house
    

    View Slide

12. 12
    Let’s find a new house
    

    View Slide

13. How is the housing price
    composed?
    

    View Slide

14. The data
    14
    One year of listed properties in Immobiliare.it
    • 8 biggest Italian cities
    • 70,000 houses
    

    View Slide

15. The property: textual features
    Textual features about the listed property
    • Number of rooms
    • Square meters
    • Energy compliance
    • Garden (yes/no)
    • etc
    15
    

    View Slide

16. The property is not an island
    16
    

    View Slide

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
    

    View Slide

18. The neighborhood/egohood
    18
    

    View Slide

19. The neighborhood/egohood
    19
    So what about the features?
    

    View Slide

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).
    

    View Slide

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
    

    View Slide

22. Neighborhood: walkability
    Walking distance to amenities (~ to Walkscore™)
    22
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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!
    

    View Slide

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.
    

    View Slide

31. Neighborhood: security perception
    31
    Which place looks more secure?
    

    View Slide

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)
    

    View Slide

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.
    

    View Slide

34. Neighborhood and place: together!
    34
    PLACE
    Characteristics of the
    census cell
    NEIGHBORHOOD
    Objective and subjective
    characteristics
    HOUSE
    Characteristics of the
    house
    

    View Slide

35. The model
    

    View Slide

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
    

    View Slide

37. 37
    Results (errors)
    Model MAE MdAPE
    Property 148, 109 23,76%
    

    View Slide

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%!
    

    View Slide

39. 39
    Global features importance
    

    View Slide

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
    

    View Slide

41. 41
    Can we explain predictions?
    House in Corso Grosseto, Turin
    

    View Slide

42. 42
    Can we explain predictions?
    House in Corso Grosseto, Turin
    THE MODEL IS INTERPRETABLE
    

    View Slide

43. 43
    Reproducibility
    Some of the principal competitors:
    • Make use of timeless, private, data
    • Do not share data
    • Do not share code
    

    View Slide

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
    

    View Slide

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!
    

    View Slide

46. View Slide

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?
    

    View Slide

48. 48
    The puzzle of housing price
    

    View Slide

49. Feedback time!
    @denadai2
    

    View Slide