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
2. 2 Let’s find a new house
3. 3 Let’s find a new house
4. 4 Let’s find a new house
5. 5 Let’s find a new house
6. 6 Let’s find a new house
7. 7 Let’s find a new house
8. 8 Let’s find a new house
9. 9 Let’s find a new house
10. 10 Let’s find a new house
11. 11 Let’s find a new house
12. 12 Let’s find a new house
13. How is the housing price composed?
14. The data 14 One year of listed properties in Immobiliare.it • 8 biggest Italian cities • 70,000 houses
15. The property: textual features Textual features about the listed property • Number of rooms • Square meters • Energy compliance • Garden (yes/no) • etc 15
16. The property is not an island 16
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
18. The neighborhood/egohood 18
19. The neighborhood/egohood 19 So what about the features?
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).
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
22. Neighborhood: walkability Walking distance to amenities (~ to Walkscore™) 22
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
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
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
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
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
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
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!
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.
31. Neighborhood: security perception 31 Which place looks more secure?
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)
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.
34. Neighborhood and place: together! 34 PLACE Characteristics of the census cell NEIGHBORHOOD Objective and subjective characteristics HOUSE Characteristics of the house
35. The model
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
37. 37 Results (errors) Model MAE MdAPE Property 148, 109 23,76%
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%!
39. 39 Global features importance
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
41. 41 Can we explain predictions? House in Corso Grosseto, Turin
42. 42 Can we explain predictions? House in Corso Grosseto, Turin THE MODEL IS INTERPRETABLE
43. 43 Reproducibility Some of the principal competitors: • Make use of timeless, private, data • Do not share data • Do not share code
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
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!
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?
48. 48 The puzzle of housing price
49. Feedback time! @denadai2