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Predicting real estate prices from the urban environment

Predicting real estate prices from the urban environment

Marco De Nadai

October 04, 2018
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  1. The economic value of neighborhoods:
    Predicting real estate prices from the urban
    environment
    MARCO DE NADAI and BRUNO LEPRI

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  2. 2
    Let’s find a new house

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  3. 3
    Let’s find a new house

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  4. 4
    Let’s find a new house

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  5. 5
    Let’s find a new house

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  6. 6
    Let’s find a new house

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  7. 7
    Let’s find a new house

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  8. 8
    Let’s find a new house

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  9. 9
    Let’s find a new house

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  10. 10
    Let’s find a new house

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  11. 11
    Let’s find a new house

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  12. 12
    Let’s find a new house

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  13. How is the housing price
    composed?

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

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

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  16. The property is not an island
    16

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  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

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  18. The neighborhood/egohood
    18

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

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  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).

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  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

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

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  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

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  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

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  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

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  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

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  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

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  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

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  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!

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  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.

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

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  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)

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  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.

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

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  35. The model

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  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

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

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  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%!

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  39. 39
    Global features importance

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  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

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

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

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

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  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

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  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!

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  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?

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  48. 48
    The puzzle of housing price

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  49. Feedback time!
    @denadai2

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