ECN 741 Urban Economics Testing Urban Models Professor

ECN 741: Urban Economics Testing Urban Models Professor John Yinger, The Maxwell School, Syracuse University, 2020

Testing Urban Models ▫ Approaches � 1. Estimate P{u} � 2. Estimate R{u} (land rent) � 3. Estimate D{u} (density) � 4. Estimate theoretically derived envelopes

Testing Urban Models ▫ Approaches � 1. Estimate P{u} � 2. Estimate R{u} (land rent) � 3. Estimate D{u} (density) � 4. Estimate derived envelope and bid functions

Testing Urban Models ▫ Dozens of studies include distance to CBD as an explanatory variable. �Some include actual commuting time (available in the census), which is endogenous! ▫ Examples of recent studies that look at several different ways of measuring time or distance: �Ottensmann, John R. , Seth Payton, and Joyce Man. 2008. “Urban Location and Housing Prices within a Hedonic Model. ” Journal of Regional Analysis and Policy 38 (1): 19 -35. �Waddell, Paul, Brian J. L. Berry, and Irving Hoch. 1993. “Residential Property Values in a Multinodal Urban Area: New Evidence on the Implicit Price of Location. ” The Journal of Real Estate Finance and Economics 7 (2) (September): 117 -141.

Testing Urban Models Example: Ottensmann et al. ▫ These scholars identify many different measures of commuting time and distance. ▫ They compare models with different measures. ▫ No clear winner emerges. ▫ Their results are summarized in the following table (where TAZ = traffic analysis zone).

Testing Urban Models

Testing Urban Models Limitations ▫ These studies do not use theory to derive functional forms. ▫ Moreover, many of them forget the most basic fact about an envelope: Moving along the envelope reflects both a change in bids from a given household type and a change from one household type to another: bidding and sorting! �The coefficient of a time or distance variable does not indicate household willingness to pay for access to jobs. �A point on a nonlinear envelope indicates a point on a household’s marginal willingness to pay (i. e. inverse demand function) for access to jobs—a point that depends on the nature of the existing market equilibrium. �But a linear (or semi-log) envelope essentially assumes that no sorting exists and cannot be given a willingness to pay interpretation.

Household Heterogeneity Bid-Rent Functions and Their Envelope P{u} Envelope Bid Functions Commuting Distance = u

Testing Urban Models ▫ Approaches � 1. Estimate P{u} � 2. Estimate R{u} (land rent) � 3. Estimate D{u} (density) � 4. Estimate derived envelope and bid functions

Testing Urban Models ▫ Land rent summarizes the derived demand for land, and some studies estimate R{u} instead of P{u}. �Example: D. P. Mc. Millen, "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach, " JUE (July 1996), pp. 100 -124. ▫ These studies do not use theoretically derived functional forms.

Testing Urban Models ▫ Approaches � 1. Estimate P{u} � 2. Estimate R{u} (land rent) � 3. Estimate D{u} (density) � 4. Estimate derived envelope and bid functions

Testing Urban Models ▫ A huge literature, going back to the 1950 s, estimates population density functions, D{u}. ▫ Fairly recent reviews can be found in: �Small and Song, "Population and Employment Densities: Structure and Change, " JUE, (November 1994), pp. 292 -313. �Anas, Arnott, and Small, “Urban Spatial Structure, ” Journal of Economic Literature (September 1998), pp. 1426 -1464 ▫ There is not much theory in this literature, apart from the (incorrect) derivation of the exponential form from an urban model, which we discussed in an earlier class.

Testing Urban Models ▫ Some informal theory is offered in the case of multiple worksites. ▫ The paper below identifies three assumptions: that different worksites are substitutes, complements, or somewhere in between. �Heikkila, E. , P. Gordon, J. I. Kim, R. B. Peiser, H. W. Richardson, and D. Dale-Johnson. 1989. “What Happened to the CBD-Distance Gradient? : Land Values in a Policentric City. ” Environment and Planning A 21 (2): 221 -232. ▫ Allocating each household to a worksite, as in the models discussed earlier, is an example of the first assumption.

Testing Urban Models ▫ Lots of room for more work on density with big data. ▫ Three-dimensional maps. �How they change over time. �How they correlate with job locations or transportation systems.

Testing Urban Models ▫ Another approach is to focus on light at night. ▫ Light is a sign of activity. �Recent photographs from space show the lack of activity after a hurricane. �Some studies look at light patterns to indicate economic growth as indicated by changes in density. �Some city light pictures are on the following links.

Testing Urban Models ▫ See, also: https: //earthobservatory. nasa. gov/images/ 77658/shanghai-at-night-a-growing-city https: //earthobservatory. nasa. gov/images/ 79800/city-lights-of-the-united-states 2012

Testing Urban Models Questions • How should one interpret the coefficient of distance to the CBD in an empirical study of housing price determinants? • What are alternative ways to measure commuting costs with one worksite? • What is the difference between the “complements” and “substitutes” views of commuting costs with many worksites.

Testing Urban Models ◦ Approaches � 1. � 2. � 3. � 4. Estimate P{u} R{u} (land rent) D{u} (density) derived envelope and bid functions

The Yinger Approach: Derive the Envelope � As explained in the last class, it is possible to derive the hedonic envelope for access to jobs with a standard, but slightly modified approach to bid functions and an assumption about the hedonic equilibrium. ◦ This is the approach in my 2020 working paper, “The Price of Access to Jobs. ” � � To the best of my knowledge, this approach is the first one to estimate the hedonic price function for access to jobs with heterogeneous households. Many studies recognize, of course, that observed prices reflect both bidding and sorting, but no previous study has been able to distinguish between these two components of observed prices.

The Price of Access to Jobs: Household Sorting, 1 � One of the advantages of my approach is that it can shed light on the determinants of household sorting in two ways. � First, recall that the sigma parameters describe the nature of the hedonic equilibrium. � The central urban theorem with heterogeneous households is that sorting depends on bid-function slopes (i. e. that σ2 is significant) and that households with steeper slopes live closer to jobs (i. e. that σ2< 0 so long as σ3 is positive). � To the best of my knowledge, these tests are new.

The Price of Access to Jobs: Household Sorting, 2 � Second, my method leads to a test of normal sorting. � Once the σ2 s are estimated, I can use the (inverted) formula for the hedonic equilibrium to estimate the relative slope parameter, ψ. � Then I can use the expression for ψ that appears in the bid-function to see how income affects bidfunction slopes. �A well-known theorem is that normal sorting arises if the income elasticity of commuting costs, χ, is smaller than the income elasticity of demand for housing, γ. � By assuming that χ is constant, I can test this theorem directly.

The Price of Access to Jobs: Household Sorting, 3 � Here � So is the algebra: a negative coefficient for the Y term supports the normal-sorting hypothesis.

The Price of Access to Jobs: Cleveland Data, 1 � My data set contains all the house sales in the Cleveland metropolitan area in 2000 � Including detailed data on ◦ Housing characteristics ◦ Neighborhood characteristics ◦ Location of jobs �A key issues is how to measure access to jobs ◦ Straight-line distance or distance along streets? ◦ Time or distance? ◦ Access to center or access to job clusters? ◦ What do home buyers perceive? (Never directly observed!)

The Price of Access to Jobs: Cleveland Data, 2 � I examine several different time and distance measures. � The ones I selected come from two sets: ◦ Measures that have the most explanatory power in a set of 9 time and 9 distance measures examined in a draft working paper by Carlos Diaz and me. ◦ Measures that play an important role in urban economic theory. The final set of measures is listed in the following table. � One Diaz/Yinger finding to note is that in Cleveland in 2000, straight-line times and distances have more explanatory power than comparable along-the-street measures. � ◦ Thanks to internet mapping programs, this may no longer be true.

The Price of Access to Jobs: Cleveland Data, 3 Table 2. Measures of Job Access Panel A: Definitions Uses tract-to-tract data From monocentric models Job sites as “complements” From models with CBD and SBD Reflects congestion Measure Definition Distance Measures (in Miles) DIST 1 Estimated actual commuting DIST 2 DIST 3 DIST 4 Minimum Maximum 7. 97 3. 01 29. 04 Straight-line distance to Terminal Tower 13. 39 1. 11 41. 36 Employment-weighted straight-line distance to worksites 13. 20 7. 27 39. 52 6. 92 0. 01 42. 25 25. 87 11. 11 46. 53 44. 51 9. 87 87. 48 time to worksites 46. 31 25. 97 86. 26 Straight-line timer to assigned worksite 32. 33 4. 24 121. 39 distance (straight line) Straight-line distance to assigned worksite Time Measures (in Minutes) TIME 1 Actual commuting time TIME 2 Estimated straight-line time to Terminal Tower TIME 3 Employment-weighted straight-line TIME 4 Mean

The Price of Access to Jobs: Cleveland Data, 4 Table 2. Measures of Job Access Distance Measures DIST 1 DIST 2 DIST 3 DIST 4 Time Measures TIME 1 TIME 2 TIME 3 TIME 4 Cross Correlations TIME 1 TIME 2 TIME 3 TIME 4 Panel B: Correlations DIST 1 1. 00 0. 64 0. 62 0. 55 DIST 2 DIST 3 DIST 4 1. 00 0. 97 0. 71 1. 00 0. 79 1. 00 TIME 1 1. 00 -0. 03 0. 10 0. 28 TIME 2 TIME 3 TIME 4 1. 00 0. 82 0. 30 1. 00 0. 61 1. 00 DIST 1 0. 26 0. 63 0. 57 0. 34 DIST 2 -0. 01 0. 99 0. 86 0. 35 DIST 3 0. 01 0. 93 0. 90 0. 45 DIST 4 0. 19 0. 67 0. 70 0. 67

The Price of Access to Jobs: Estimating Strategy �I estimate the envelopes and bid functions using the following steps: � Step 1: Estimate the hedonic ◦ Step 1 A: Estimate a house sales regression with neighborhood (CBG) fixed effects. ◦ Step 1 B: Estimate a hedonic for neighborhood traits, including access to jobs. � Step 2: Estimate the bid functions ◦ Calculate ψ (as explained earlier) and regress it on the determinants of a household’s relative bid-function slope.

The Price of Access to Jobs: Estimating Strategy, Stage 1 A � � The regression in stage 1 A has 22, 880 observations (= house sales); the dependent variable is the log of house value. It includes: ◦ 17 housing traits (such as square footage, house age, and lot size), ◦ 18 variables to measure the difference in a location’s traits (such as distance to an elementary school or to Lake Erie) between the house’s actual location and the center of its CBG, ◦ and 1, 665 CBG fixed effects. � � The R-squared is 0. 7893. The neighborhood (=CBG) fixed effects are highly significant. Stage 1 A Regression

The Price of Access to Jobs: Estimating Strategy, Stage 1 B, 1 � � � The regressions in stage 1 B have 1, 665 observations (= CBGs); the dependent variable is a CBG’s fixed effect from stage 1 A (plus the constant). The regressions include 50 controls for locational traits (such as school quality, crime rate, aid pollution, access to parks) plus county and worksite fixed effects. This regression is conducted for each measure of job access. The R-squared values are all close to 0. 70. Most of the control variables are statistically significant (with errors clustered at the school district level). List of Control Variables

The Price of Access to Jobs: Estimating Strategy, Stage 1 B, 2 � � I start with linear, log, and quadratic specifications. The linear term is negative and significant for 7 of the 8 cases, but, as explained earlier, it does not identify any structural parameters. The log term is negative and significant in 5 of the 8 cases, but this result is consistent with both no time costs and no operating costs (and does not identify structural parameters). Both terms of the quadratic form are significant (negative and positive) for DIST 1, DIST 3, and TIME 1.

The Price of Access to Jobs: Preliminary Results, 1 Table 3: Access Envelopes Estimated with Simple Forms Linear Log Quadratic, 1 st Term Quadratic, 2 nd Term Distance Measures (in Miles) DIST 1 Estimated actual commuting dist. (straight line) Coefficient t-Statistic DIST 2 DIST 3 DIST 4 -0. 00259 -0. 05066 -0. 02390 0. 00075 (-0. 97) (-1. 72) (-2. 39*) (2. 47*) Straight-line distance to Terminal Tower Coefficient -0. 00830 -0. 10296 -0. 01492 0. 00014 t-Statistic (-3. 64**) (-2. 17*) (-1. 49) (0. 75) Employment-weighted straight-line distance to worksites Coefficient -0. 00946 -0. 20813 -0. 02919 0. 00043 t-Statistic (-4. 42**) (-5. 24**) (-3. 34**) (2. 29*) Straight-line distance to assigned worksite Coefficient -0. 00449 -0. 01195 -0. 00624 -0. 00002 t-Statistic (-5. 53**) (-1. 36) (-1. 41) (-0. 20) Dependent variable is CBG fixed effect; 1, 665 observations; many controls included.

The Price of Access to Jobs: Preliminary Results, 2 Table 3: Access Envelopes Estimated with Simple Forms Linear Log Quadratic, 1 st Term Quadratic, 2 nd Term Time Measures (in Minutes) TIME 1 Actual commuting time Coefficient -0. 00307 -0. 08781 t-Statistic (-2. 77**) -0. 01462 0. 00021 (-2. 28*) (2. 03*) TIME 2 Estimated straight-line time to Terminal Tower Coefficient -0. 00498 -0. 17443 t-Statistic (-2. 93**) (-2. 06*) -0. 00315 -0. 00002 (-0. 62) (-0. 44) TIME 3 Employment-weighted straight-line time to worksites Coefficient -0. 00437 -0. 18173 t-Statistic (-5. 06**) (-4. 72**) -0. 00188 -0. 00003 (-0. 44) (-0. 61) -0. 00069 0. 00000 (-0. 59) (-0. 25) TIME 4 Straight-line time to assigned worksite Coefficient t-Statistic -0. 00094 -0. 02033 (-2. 05*) (-1. 68) Dependent variable is CBG fixed effect; 1, 665 observations; many controls included.

The Price of Access to Jobs: Estimating Strategy, Stage 1 B, 3 � Then I turn to my specification with different assumed values for the key parameters that are available in the literature: ◦ σ3 = ½, 1, and 2 ◦ γ = 0. 3 and 1. 0 (based on the literature) plus 1. 5 (needed for a solution when σ3 = 2) ◦ λ = 0. 3 and 1. 0 (based on the literature) � This leads to 14 cases (not 18 because γ = 1. 5 is required when σ3 = 2).

The Price of Access to Jobs: Main Results, 1 � The results are significant (5% level) with the expected negative sign in every case for DIST 1, DIST 3, and TIME 1—and only once for the other 5 measures of job access. � The results imply that the price of housing at the location with the least-valued access compared to the location with the best access is about 12% lower for DIST 1, 26% lower for DIST 3, and 34% lower for TIME 1. � These results for DIST 1, DIST 3, and TIME 1 strongly support theorem that household sorting depends on the slopes of household bid functions.

The Price of Access to Jobs: Main Results, 2 Table 4. Illustrative Access Envelopes Estimated with Theoretically Derived Forms (Part A) Assumed Value of Access Measure Estimated Value of RSquared DIST 3 λ γ σ3 0. 5 σ1 1083. 64 (4. 22**) σ2 -200. 77 (-2. 27*) DIST 3 0. 3 1. 0 33. 66 (6. 60**) -4. 38 (-2. 39*) 0. 7018 DIST 3 0. 3 1. 5 2. 0 5. 88 (11. 20**) -0. 48 (-2. 47*) 0. 7019 DIST 1 1. 0 0. 5 298. 73 (8. 22**) -274. 03 (-2. 76**) 0. 6967 DIST 1 1. 0 1. 5 1. 0 16. 55 (13. 75**) -10. 17 (-2. 60**) 0. 6967 DIST 1 1. 0 1. 5 2. 0 4. 00 (24. 03**) -1. 40 (-2. 51*) 0. 6966 0. 7016 Dependent variable is CBG fixed effect; 1, 665 observations; many controls included.

The Price of Access to Jobs: Main Results, 3 Table 4. Illustrative Access Envelopes Estimated with Theoretically Derived Forms (Part B) Access Measure Assumed Value of Estimated Value of RSquared DIST 1 (IV) λ γ σ3 1. 0 σ1 25. 08 (1. 98*) σ2 -6. 04 (-1. 00) TIME 1 1. 0 0. 3 0. 5 1207. 76 (7. 12**) -577. 41 (-2. 15*) 0. 6963 TIME 1 1. 0 34. 93 (12. 13**) -10. 93 (-2. 10*) 0. 6962 TIME 1 0. 3 1. 5 2. 0 5. 92 (22. 17**) -1. 09 (-2. 09*) 0. 6952 DIST 2 0. 3 1. 0 0. 5 -72723. 48 -1847. 04 0. 7004 (-34. 70**) (-2. 33*) 1932. 34 (2. 05*) -3591. 63 (-1. 26) DIST 4 1. 0 1. 5 0. 6723 0. 6989 Dependent variable is CBG fixed effect; 1, 665 observations; many controls included.

The Price of Access to Jobs: The Surprising Upturn, 1 � � � These results also contain a surprise: The envelopes turn up when job access is poor. Although it applies only to a few observations (16 to 59 out of 1, 665), this result obviously needs explanation. This turn-up is not created by my functional form. ◦ It also appears in the related quadratic specifications. � It suggests that models of access to jobs may be missing something: ◦ Perhaps long commutes have greater reliability or some other trait that household value and that is not accounted for by distance or time alone. ◦ Or, despite my data gathering efforts, I may have omitted a non-transportation locational trait that is correlated with long commutes (in distance and time!).

The Price of Access to Jobs: The Surprising Upturn, 2 Figure 3. Access Envelopes for Various Access Measures Panel A: Distance Measures with λ = γ = σ3 = 1

The Price of Access to Jobs: The Surprising Upturn, 3 � The most likely candidate for a missing variable is speed. Recall that operating costs associated with distance assume that speed is constant. ◦ In fact, speed increases with distance (in theory and in my data). � � One way to address this issue is to assume that DIST 1 is measured with error (relative to what home buyers perceive) and to estimate the model with instruments. This approach greatly diminishes the “turn-up” but does not eliminate it. ◦ Moreover, the turn-up also appears—to a much smaller degree—with TIME 1, ◦ And good instruments are difficult to find. Instruments

The Price of Access to Jobs: The Surprising Upturn, 4 Figure 3. Access Envelopes for Various Access Measures Panel B: Distance and Time Measures with λ = γ = σ3 = 1

The Price of Access to Jobs: Impact of Assumed Parameters, 1 � DIST 3 has the highest explanatory power of any jobaccess measure, although the differences are not significant. � The envelopes for DIST 3 are very similar for various values of the assumed parameters. � The turn-up for DIST 3 affects very few observations. � This result supports the “complements” approach to multiple worksites—not the “substitutes” view in urban models. ◦ An urban modelling opportunity!

The Price of Access to Jobs: Impact of Assumed Parameters, 2 Figure 4. Access Envelopes for DIST 3 Panel A. Envelopes with σ3 = 1

The Price of Access to Jobs: Impact of Assumed Parameters, 3 Figure 4. Access Envelopes for DIST 3 Panel B. Envelopes with λ = 0. 3 and γ = 1. 5

The Price of Access to Jobs: Sorting Results, 1 � � � As explained earlier, my approach yields an estimate of the relative slope of a bid function, ψ, which makes it possible to estimate the determinants of this slope. One determinant of this slope is income; a negative coefficient for income indicates normal sorting. I have two sources of income data ◦ HMDA data, which give income for people actually buying housing, who are in my sales data set. � These data cover the right people but are only available by tract and only include a few household traits. ◦ Census data, which gives CBG characteristics. � These data have many household traits, but cover all the people in a CBG, not just new buyers.

The Price of Access to Jobs: Sorting Results, 2 � The results strongly support normal sorting for DIST 3; the coefficient of income is negative and significant regardless of the income measure. � In the case of DIST 1, the income coefficients are negative and significant for the Census income measure but not for the HMDA income measure. � In the case of TIME 1, the income coefficients are negative and significant for the Census income measure and negative and weakly significant (5 of the 14 cases at the 5% level) for the HMDA income measure.

The Price of Access to Jobs: Sorting Results, 3 Table 5: Illustrative Normal Sorting Tests Data Source Assumed Value of λ γ σ3 HMDA 1. 0 0. 3 0. 5 HMDA 0. 3 1. 0 HMDA 1. 0 HMDA (IV) 1. 0 Census 1. 0 0. 3 0. 5 Census 0. 3 1. 0 Census 1. 0 Census (IV) 1. 0 Income Coefficient for: DIST 1 DIST 3 TIME 1 -0. 0893 (-3. 45**) -0. 1035 (-5. 76**) -0. 1006 (-4. 62**) -0. 0760 (-4. 11**) -0. 1962 (-5. 57**) -0. 2620 (-6. 41**) -0. 2786 (-5. 93**) -0. 1267 (2. 37*) 0. 0351 (1. 48) 0. 0075 (0. 28) 0. 0159 (0. 55) -0. 0484 (-1. 71) -0. 0614 (-2. 05*) -0. 0544 (-1. 85) -0. 1961 (-3. 88**) -0. 2609 (-4. 35**) -0. 2953 (-4. 67**) -0. 1709 (-2. 37*) -0. 1882 (-2. 58**) -0. 1858 (-2. 49*) The dependent variable is an estimate of ψ; there are between 1, 606 and 1, 649 observations; household traits are included as controls. Full Results Switching Model

The Price of Access to Jobs: Conclusions � My paper is the first to estimate job-access bid functions and envelopes with heterogeneous households. � Linear and log specifications cannot estimate structural parameters of the hedonic equilibrium. � The form I derive provides significant structural parameters using 3 different job-access measures: DIST 1, DIST 3, and TIME 1. � The results support the hypothesis that household sorting depends on bid-function slopes. � The results support “normal” sorting under most, but not all circumstances.

The Price of Access to Jobs: Remaining Questions � The results in my paper leave may questions unanswered. ◦ Why do such different measures of job access have similar explanatory power? ◦ Do other theoretically based functional forms yield significant structural parameters with job-access measures other than DIST 1, DIST 3, and TIME 1? ◦ Is the “complements” view of job access supported by estimated envelopes in other metropolitan areas? ◦ Have perceptions of job access changed with the advent of mapping software? ◦ Is the upward turn in bid-function envelopes an important behavioral issue or simply a flaw in my theory or methodology? ◦ Why does normal sorting arise with some measures of job access (with some measures of income) but not others?

The Price of Access to Jobs Appendix Table B 3. Illustrative ψ Regression for DIST 1 Variable Definition Regression Based on HMDA Data (by Census tract) Coefficient t-Statistic lav_inc Log of average HMDA income -0. 0834 (-4. 39**) ploan_blk 2 Pr(Buyer is African-American) 0. 1054 (4. 92**) ploan_his 2 Pr(Buyer is Hispanic) 0. 2655 (5. 85**) ploan_sinm Pr(Buyer is single male) 0. 0823 (3. 28**) ploan_sinf Pr(Buyer is single female) -0. 1019 (-2. 37* ) ploan_cupm Pr(Buyer is male couple) 0. 0890 (1. 77 ) ploan_cupf Pr(Buyer is female couple) 0. 0422 (0. 69 ) ploan_fha Pr(Buyer uses FHA loan) 0. 0592 (3. 19**) ploan_vet Pr(Buyer uses VA loan) -0. 4218 (-1. 16 ) fillin HMDA data missing -0. 0854 (-1. 84 ) constant 0. 1192 (1. 50 ) Dependent variable is estimated ψ; 1, 640 observations (omitting 25 observations with negative ψ); standard errors are clustered at the school-district level; significance: * = 5%; ** = 1%; assumed values for exogenous parameters are γ = λ = 1 and σ 3 = 0. 5

The Price of Access to Jobs: Estimating Strategy, Stage 2, Alternative � The main sorting results are based on the downward-sloping regions of the estimated envelopes. � The few observations (between 16 and 59) on the upward sloping regions are simply dropped. � Another possibility is to estimate an endogenous switching model in which households select one of these regions and then have demand for job access within the region they select. � These models do not converge using DIST 3, but sometimes yield results using DIST 1 and TIME 1.

The Price of Access to Jobs Estimated ψ is the dependent variable; 1, 665 observations; Wald test rejects independent equations. Appendix Table B 4. Illustrative Endogenous Switching Regression for DIST 1 Variable Definition Inside Access-Envelope Minimum lav_inc Log of avg. HMDA income in tract ploan_blk 2 Pr(buyer is African-American) ploan_his 2 Pr(Buyer is Hispanic) ploan_sinm Pr(Buyer is single male) ploan_sinf Pr(Buyer is single female) ploan_cupm Pr(Buyer is male couple) ploan_cupf Pr(Buyer is female couple) ploan_fha Pr(Buyer uses FHA loan) ploan_vet Pr(Buyer uses VA loan) fillin HMDA data missing Outside Access-Envelope Minimum lav_inc Log of avg. HMDA income in tract ploan_blk 2 Pr(buyer is African-American) ploan_his 2 Pr(Buyer is Hispanic) ploan_sinm Pr(Buyer is single male) ploan_sinf Pr(Buyer is single female) ploan_fha Pr(Buyer uses FHA loan) ploan_vet Pr(Buyer uses VA loan) fillin HMDA data missing Selection Equation Nocar_cbg % of households with no car in CBG Coefficient z -0. 09863 0. 10636 0. 32843 0. 11114 -0. 02703 0. 01545 -0. 04273 0. 03778 0. 19258 -0. 04713 (-5. 54**) (6. 41**) (9. 38**) (4. 72**) (-0. 95 ) (0. 22 ) (-0. 44 ) (1. 95 ) (4. 70**) (-1. 39 ) -6. 34767 19. 50171 38. 98210 2. 88407 -6. 67123 8. 51318 -3. 83387 -1. 46736 (-4. 67**) (2. 92**) (2. 24* ) (2. 20* ) (-6. 59**) (5. 63**) (-6. 17**) (-2. 99**) -0. 00840 (-1. 29 ) -0. 12064 0. 00553 (-3. 18**) (0. 59 ) Pctforeign_cbg Pctkids_cbg % of pop. foreign-born in CBG % of households with kids in CBG Pctmar_cbg % of households married couple in CBG 0. 05012 (2. 58**) Bluecoll_cbg lav_inc % of workers with blue-collar jobs in CBG Log of avg. HMDA income in tract 0. 03613 0. 35425 (3. 25**) (2. 72**)

The Price of Access to Jobs: Instruments for DIST 1 � The instruments used for DIST 1 are: ◦ The population density in a CBG’s zip code in 1990 ◦ The distance of the CBG from the point with average latitude and longitude in the metropolitan area ◦ The difference between the employment-weighted straight-line and google distances to worksites, and the square of this difference. � � These instruments pass weak-instrument (F = 12. 7) and exogeneity tests. These instruments do not work for other job-access measures.

The Price of Access to Jobs: Step 1 A Regression Appendix Table B 1. Results for First-Stage Hedonic with Neighborhood Fixed Effects Variable One Story Brick Basement Garage Air Cond. Fireplaces Bedrooms Full Baths Part Baths Age of House Area Lot Area Outbuildings Porch Deck Pool Date of Sale Commute 1 a Commute 2 a Commute 3 a Commute 4 a Commute 5 a Definition House has one story House is made of bricks House has a finished basement House has a garage House has central air conditioning Number of fireplaces Number of bedrooms Number of full bathrooms Number of partial bathrooms Log of the age of the house Log of square feet of living area Log of lot size Number of outbuildings House has a porch House has a deck House has a pool Date of house sale (January 1=1, December 31=365) Employment wtd. commuting dist. (house. CBG), worksite 1 Employment wtd. commuting dist. (house. CBG), worksite 2 Employment wtd. commuting dist. (house. CBG), worksite 3 Employment wtd. commuting dist. (house. CBG), worksite 4 Employment wtd. commuting dist. (house. CBG), worksite 5 Coefficient - 0. 0072 0. 0153*** 0. 0308*** 0. 1414*** 0. 0254*** 0. 0316*** - 0. 0082*** 0. 0601*** 0. 0412*** - 0. 0839*** 0. 4237*** 0. 0844*** 0. 1320*** 0. 0327*** 0. 0545*** 0. 0910*** Std. Error 0. 0050 0. 0052 0. 0050 0. 0067 0. 0055 0. 0038 0. 0028 0. 0042 0. 0041 0. 0032 0. 0086 0. 0037 0. 0396 0. 0073 0. 0053 0. 0180 0. 0002*** 0. 0000 - 0. 0952*** 0. 0272 - 0. 0991*** 0. 0321 - 0. 1239*** 0. 0302 - 0. 1012*** 0. 0295 - 0. 0942*** 0. 0344 Appendix Table B 1. Results for First-Stage Hedonic with Neighborhood Fixed Effects Dist. to Pub. Schoola Elem. School Scorea Dist. to Pri. School Distance to Hazard Distance to Eriea Distance to Ghettoa Distance to Airporta Dist. to CBG Center Historic Districta Dist. to nearest pub. elem. school in district - 0. 0032 (house-CBG) Avg test scores of nearest pub. elem. school 0. 0170 rel to dis (house-CBG) Distance to nearest private school (house- 0. 0168*** CBG) Dist. to nearest environmental hazard (house 0. 0332*** CBG) Dist. to Lake Erie (if < 2; house-CBG) - 0. 0021** Dist. to black ghetto (if < 5; house-CBG) - 0. 1020*** Dist. to Cleveland airport (if < 10; house-CBG) Distance from house to center of CBG 0. 0259** - 0. 0239*** 0. 0061 0. 0197 0. 0057 0. 0082 0. 0010 0. 0331 0. 0122 0. 0074 In historic district on national register (house 0. 0120 0. 0178 CBG) a Elderly Housing Within 1/2 mile of elderly housing project - 0. 0327* 0. 0194 (house-CBG) Family Housinga Within 1/2 mile of small family hsg. project 0. 0836** 0. 0403 (house-CBG) Large Hsg Within 1/2 mile of large fam housing proj - 0. 0568** 0. 0257 Projecta (>200 units; house-CBG) High Crime Distance to nearest high-crime location 0. 0701*** 0. 0246 (house-CBG) Notes: Dependent variable = log of transaction amount; 22, 880 observations; R 2 =. 7893; F(31, 21180) = 369. 17 (significant at 0. 000 level); 1, 665 fixed effects with F(1664, 21, 180) = 8. 534 (significant at 0. 000 level); estimated with “areg” command in Stata. Distances are measured in miles. A * (**) [***] indicates statistical significance at the 10 (5) [1] percent level. a. Variable added to the original Brasington data set. Source: Yinger (2015 b).

The Price of Access to Jobs: Control Variable in Step 1 B Variable High School Passing Rate (a) Elementary Value Added (a) Share Black (a) Share Hispanic (a) Share Minority Teachers (a) School Tax Rates Appendix Table B 2. Control Variables in Regressions for Job-Access Envelopes Definition School district share of students who entered 12 grade and passed 5 state tests Within-cohort increase in tests, 4 th to 6 th grade, nearest elementary school Black population share in CBG Hispanic population share in CBG Share minority teachers in school district School district income and property tax rates Effective city property tax rate beyond school tax and City Tax Rate exemption rate No A-to-S Dummy: No assessment/sales data to correct tax rate Not a City CBG not in a city Crime Lowhigh Low property, high violent crime in CBG Crime Highlow High property, low violent crime in CBG Crime Highhigh High property and violent crime in CBG Crime Hotspot 1 CBG within ½ mile of crime hot spot Crime Hotspot 2 CBG ½ to 1 mile from crime hot spot Crime Hotspot 3 CBG 1 to 2 miles from crime hot spot Crime Hotspot 4 CBG 2 to 5 miles from crime hot spot Police CBG receives police from village, township, or county City Population of city (if CBG in a city) Notes: (a) Indicates that the variable is specified as an amenity with a price elasticity of -0. 75, as discussed in the text; (b) indicates that the variable is accompanied by a second variable that is distance from the amenity interacted with the dummy indicating that the amenity is within a certain distance of the CBG; the worksites are the ones identified in the text; the counties are the five counties that make up the Cleveland MSA. Variable Smog (b) Near Hazard (b) Near Public (b) Near Private (b) Lakefront (b) Snowbelt Ghetto Near Airport (b) Local Amenities Freeway Railroad Shopping Hospital Small Airport Big Park Historic District Near Elderly PH Near Small Fam. PH Near Big Fam. PH Worksite County Definition CBG within 20 miles of air pollution cluster CBG is within 1 mile of a hazardous waste site CBG is within 2 miles of public elem. school CBG is within 5 miles of a private school CBG within 2 miles of Lake Erie Quadratic for distance of CBG from Lake Erie East of beltway CBG in the black ghetto CBG within 5 miles of ghetto center CBG within 10 miles of Cleveland airport Number of parks, golf courses, rivers, or lakes within ¼ mile of CBG within ¼ mile of freeway CBG within ¼ mile of railroad CBG within 1 mile of shopping center CBG within 1 mile of hospital CBG within 1 mile of small airport CBG within 1 mile of regional park CBG within an historic district CBG within ½ mile of elderly public housing CBG within ½ mile of small family public housing CBG within ½ mile of large family public housing (>200 units) Assigned worksite fixed effects County fixed effects