New Sources. Improving Understanding through Measurement. Surveys & Records. Human Activity. Built Environment. Field Surveys

Transcription

1 Improving Understanding through Measurement Surveys & Records New Sources Field Surveys Human Activity Cellphone Records Mobility Data Social Networks Built Environment Street-level Imagery Aerial Imagery Satellite Imagery

2 Nowcasting with Kim and Luca

3 Street-level Imagery Images of streets collected by vehicle-mounted cameras Accompany maps in interactive web interfaces Google Street View, Microsoft Streetside, Tencent (China), Wonobo (India)

4 Google Street View

5 pulse.media.mit.edu Salesses, Schechtner, and Hidalgo (2013) Crowdsourced urban appearance survey

6 4,000 Images From New York, Boston, Linz and Salzburg More than 8,000 Unique Participants from 91 countries More than 200,000 Pairwise Comparisons

7 Converting Pairwise Comparisons to Ranked Scores MIcrosoft Trueskill [Herbrich et al.2007]

8 Example Images Low Streetscore

9 Example Images High Streetscore

10 #Total Street blocks ~1,000,000 #Sampled Street blocks 1,700

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12 Image Features Sky Building Ground Trees GIST Texton Maps CIELAB Color Histograms

13 Prediction Performance R 2 = 0.54 True Streetscore Predicted Streetscore

14 Improved performance with CNNs R 2 = 0.72 Features extracted from Alexnet + linear SVR

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16 Streetscore = 1.8/10 Streetscore = 7.2/10 Change in Streetscore is a proxy for more general change in the built environment Glaeser, Hidalgo, Kominers, Naik and Raskar

17 Streetchange : Significant Decline

18 Urban Growth in New York

19 Urban Growth in Boston

20 Testing Classical Theories of Urban Change Data from New York, Boston, Washington DC, Baltimore, and Detroit 2514 census tracts, 1.5 million street blocks Socioeconomic data from 2000 Census Test classical theories of urban change

21 The Human Capital Agglomeration Theory Glaeser et al. (1995, 2009), Ciccone and Hall (1996), Bettencourt (2013) Population Density and College Education are strongest predictors of future growth in neighborhoods Controlling for race, income, age, housing costs etc.

22 Positive urban change occurs in geographically and physically attractive areas which have a concentration of educated population

23 Tipping Theory Grodzins (1957), Schelling (1969) Neighborhoods with better initial appearances experience larger positive improvement

24 Invasion Theory Burgess (1925) Neighborhoods are more likely to improve when they are close to downtown and/or other neighborhoods perceived as safe

25 Predicting Income from Imagery Proof-of-concept experiment for the U.S. Median Income of the Census Block group: $60,000

26 Training Examples $74,000 Computer Vision Predicted Income $38,000 $18,000 Image Features Derived from Pixels $54,000

27 Training Sample New York Income R^2 = 0.85

28 Testing Sample New York Income R^2 = 0.81

29 New York City R^2 = 0.81 Boston R^2 = 0.86

30 Housing Price Evaluation Obvious interest in evaluation for property tax assessors in the U.S. and elsewhere. Property values are also interesting but often because we want to place a dollar value on local public goods. Visual recognition can help this if we have an independent price measure (in this case, the visuals become the hedonic attribute). Predicted price may or may not be useful as outcomes.

31 Predicting Property Prices from Imagery

32 Ongoing Work: Boston Property Prices Dataset Glaeser, Kincaid, Naik Single Family Homes, condominiums from Greater Boston area. Sold between Includes data on both physical characteristics, and subjective measures of quality from city assessor s office. Includes Streetscore measures for all properties, evaluated from images captured between 2009 and 2014.

33 Two Types of Hypotheses The Architecture Puzzle: Does appearance drive price? Its Urban Design Cousin: Does neighbor appearance drive price? Do changes in incentives influence investment that impact appearance? Foreclosure, homeownership, resale.

34 We work with locationorthogonalized residuals

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36 The Computer Prediction Issue As a massive R-Squared booster, this failed. Yes, it did better than the in-person assessors. But relative to a basic housing-price hedonic, this doesn t explain a great deal of the variation. Will this generalize? Does this tell us something meaningful about the actual market value of aesthetics?

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38 T-Stat vs. R-Squared As economists, we are typically trained to care about point estimates not r-squareds. Computer scientistics want to predict. But the strength of aesthetics is quite real. Recall this algorithm is trained out of sample. The coefficients are highly significant and pretty large in magnitude One standard deviation of the index is $55,000

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40 Exterior vs. Interior (Zillow) Images

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42 A Policy Question The value of aesthetic appeal to the owner has little or no policy relevance whatsoever. The value of aesthetic appeal to the neighbor matters for many policies. Zoning, historic preservation, local style based policies are justified based on aesthetic externalities. The problem is omitted area characteristics (nice homes mean rich neighbors).

43 Our test is geography Neighbors who are on the same street are visible. Neighbors who are close but not on the same street may be less so. Consequently, the difference in the effect between on-street neighbors and off-street neighbors provides some ability to quantify the aesthetic appeal.

44 Our statistical approach We regress individual price on neighbor s price (the OLS coefficient has little interest to us but is reported). We IV for neighbor s price with neighbor s visuals. This gives a scaling for the impact of visual: Cov(Y,Z)/Cov(X,Z)=Cov(Y,Z)/Var(Z)/Cov (X,Z)/Var(Z) We control for individual home characteristics, but might control for neighbors as well.

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46 Visuals as an Outcome Sometimes we have direct measures of home investment, but visuals provide us with a means of assessing investment. With this we can test hypotheses about how incentives impact home investment. I m going to highlight foreclosure ownership and resale. The latter is important for repeat-sales indices

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48 Remodeling: Five Nearest Neighbor Propensity Score

49 The Foreclosure Hypothesis It is well known that foreclosure brings in much less than the book value of the home. Campbell, Giglio and Pathak document the price impact of a forced sale. Some have claimed that the foreclosure effect is due to destruction of the physical home. This rhetoric was very heated during the foreclosure crisis.

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51 Our Test We take Boston homes that were foreclosure from We match with 5 nearest neighbors using a propensity score based either on initial visuals or initial visuals plus other characteristics (including location). We then compare the difference in visuallypredicted price (No Real Price).

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53 Resale and Ownership Hypotheses Owners take better care of their externals right before they sell. This causes repeat-sales indices to be biased upwards relative to the average home. Shilling, Sirmans and Dibrow (1991) found that rented single family homes lost 1% of value per year relative to owned. This must have to do with non-contractible effort for services that are customarily done by homeowners (maybe not externals).

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55 What Does This Mean? It isn t clear how many of these Boston results will generalize but it is worth knowing. The larger point is that computer vision is with us and enables us to measure the built environment as never before. This means that we can test whether the visuals matter for things like price. And test what causes the visuals to change.