# RECORD 98c105ab42ddbb39da40f571a9e40579ebb0b16f
# TITLE: The impact of the Coronavirus pandemic on New York City real estate: First evidence
# YEAR: 2021.0  VENUE: Journal of Regional Science  DOI: 10.1111/jors.12591
# AUTHORS: Jeffrey P. Cohen; Felix L. Friedt; Jackson P. Lautier
# PAGES: 38  CANDIDATE TABLE PAGES (1-based): [22, 25, 28, 31]
# ABSTRACT: Abstract We investigate whether pandemic‐induced contagion disamenities and income effects arising due to COVID‐related unemployment adversely affected real estate prices of one‐ or two‐family owner‐occupied properties across New York City (NYC). First, ordinary least squares hedonic results indicate that greater COVID case numbers are concentrated in neighborhoods with lower‐valued properties. Second, we use a repeat‐sales approach for the period 2003–2020, and we find that both the possibility of contagion and pandemic‐induced income effects adversely impacted home sale prices. Estimates suggest sale prices fell by roughly $60,000 or around 8% in response to both of the following: 1000 additional infections per 100,000 residents and a 10‐percentage point increase in unemployment in a given Modified Zip Code Tabulation Area (MODZCTA). These price effects were more pronounced during the second wave of infections. On the basis of cumulative MODZCTA infection rates through 2020, the estimated COVID‐19 price discount ranged from approximately 1% to 50% in the most affected neighborhoods, and averaged 14%. The contagion effect intensified in the more affluent, but less densely populated NYC neighborhoods, while the income effect was more pronounced in the most densely populated neighborhoods with more rental properties and greater population shares of foreign‐born residents. This disparity implies the pandemic may have been correlated with a wider gap in housing wealth in NYC between homeowners in lower‐priced and higher‐priced neighborhoods.


===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) =====

1 
The Impact of the Coronavirus Pandemic on New York City  
Real Estate: First Evidence 
 
 
Abstract: We investigate whether pandemic-induced contagion disamenities and income effects arising 
due to COVID-related unemployment adversely affected real estate prices of one- or two-family owner-
occupied properties across New York City (NYC). First, OLS hedonic results indicate that greater COVID 
case numbers are concentrated in neighborhoods with lower-valued properties. Second, we use a repeat-
sales approach for the period 2003 to 2020, and we find that both the possibility of contagion and 
pandemic-induced income effects adversely impacted home sale prices. Estimates suggest sale prices fell 
by roughly $60,000 or around 8% in response to both of the following: 1,000 additional infections per 
100,000 residents; and a 10-percentage point increase in unemployment in a given Modified Zip Code 
Tabulation Area (MODZCTA). These price effects were more pronounced during the second wave of 
infections. Based on cumulative MODZCTA infection rates through 2020, the estimated COVID-19 price 
discount ranged from approximately 1% to 50% in the most affected neighborhoods, and averaged 14%. 
The contagion effect intensified in the more affluent, but less densely populated NYC neighborhoods, 
while the income effect was more pronounced in the most densely populated neighborhoods with more 
rental properties and greater population shares of foreign-born residents. This disparity implies the 
pandemic may have been correlated with a wider gap in housing wealth in NYC between homeowners in 
lower-priced and higher-priced neighborhoods.  
Keywords: COVID-19, Hedonic, Repeat Sales, Price Discount, Housing Wealth Inequality 
JEL Codes: R31 
 
∗ Jeffrey P. Cohen, Kinnard Scholar in Real Estate and Professor of Finance, Department of Finance and Center for Real Estate 
and Urban Economic Studies, School of Business, University of Connecticut, 2100 Hillside Rd, Storrs, CT 06269; and Research 
Fellow, Institute for Economic Equity, Federal Reserve Bank of St. Louis. jeffrey.cohen@business.uconn.edu 
† Felix L. Friedt, Assistant Professor of Economics, Department of Economics, Macalester College, 1600 Grand Avenue, Saint 
Paul, MN 55105; ffriedt@macalester.edu.  
‡ Jackson Lautier, Department of Statistics, University of Connecticut, Philip E. Austin Building, 215 Glenbrook Road, Storrs, 
CT 06269-4120, jackson.lautier@uconn.edu.  
 
Acknowledgements: Cohen’s work on this paper was completed in part while he has been a Research Fellow at the Institute for 
Economic Equity, Federal Reserve Bank of St. Louis. Lautier’s efforts on this paper were based upon work supported in part by the 
National Science Foundation Graduate Research Fellowship under Grant No. DHE 1747453.  The authors would also like to thank 
Jason Barr for his helpful suggestions on the availability of related New York City datasets. Session participants at the 2022 
ASSA/AREUEA Annual Meetings (virtual) provided helpful comments. Any remaining errors are our own. The views expressed 
in this paper are those of the authors and do not necessarily represent the Federal Reserve Bank of St. Louis, the Federal Reserve 
Board, or the Federal Reserve System. Revised 1/27/2022. 
Jeffrey P. Cohen∗ 
Felix L. Friedt† 
Jackson P. Lautier‡ 
University of Connecticut 
Macalester College 
University of Connecticut 


===== PAGE 22 (score 162, numeric-paren 141, table-words 3, var-words 15) =====

22 
 
Table 2: Changes in Pre- and Post-Pandemic NYC Real Estate Market Characteristics 
 
                                    (1) 
(2) 
(3) 
(4) 
(5) 
(6) 
(7) 
(8) 
(9) 
(10) 
 
Sales of once-sold Properties 
Sales of repeatedly-sold Properties 
Characteristic 
Pre- 
Pre- 
Post- 
Post- 
∆ in 
Pre- 
Pre- 
Post- 
Post- 
∆ in 
Mean 
S.D. 
Mean 
S.D. 
Means 
Mean 
S.D. 
Mean 
S.D. 
Means 
Panel A: First Wave of Infections (03/01/2019-07/31/2019 vs. 03/03/2020-07/31/2020) 
Sale Price ($ ‘000) 
784.74 
(600.4) 
793.64 
(579.2)   
8.90 
660.50 
(470.3) 
763.98 
(384.9) 
103.48*** 
# of Sales 
81.20 
(60.3) 
49.43 
(34.7) 
-31.78*** 
10.24 
(6.5) 
5.09 
(3.2) 
-5.15*** 
Unemployment Rate (%) 
4.04 
(2.8) 
14.07 
(8.1) 
10.03*** 
5.13 
(3.4) 
15.48 
(8.1) 
10.35*** 
Renovated Home (yes==1) 
0.04 
(0.2) 
0.05 
(0.2) 
0.01 
0.03 
(0.2) 
0.04 
(0.2) 
0.01 
Building Area (’00 sqft) 
24.34 
(164.6) 
28.95 
(222.0) 
4.61 
16.57 
(6.2) 
17.52 
(7.4) 
0.95 
Lot Area (’000 sqft) 
7.25 
(109.4) 
10.93 
(162.4) 
3.68 
2.88 
(1.5) 
2.90 
(1.9) 
0.02 
# of Floors 
2.03 
(0.5) 
2.03 
(0.5) 
-0.00 
2.04 
(0.5) 
2.09 
(0.5) 
0.05 
Basement (yes==1) 
1.00 
(0.0) 
1.00 
(0.0) 
0.00 
1.00 
(0.0) 
1.00 
(0.0) 
0.00 
Population (’000) 
53.94 
(25.2) 
54.89 
(25.3) 
0.95 
54.27 
(24.9) 
57.51 
(24.0) 
3.24 
Share of White Residents (%) 
39.46 
(17.1) 
38.05 
(16.9) 
-1.41*** 
38.56 
(18.3) 
38.96 
(18.0) 
0.40 
Share of Rental Units (%) 
45.95 
(19.9) 
46.45 
(20.0) 
0.50 
47.16 
(20.5) 
48.07 
(21.5) 
0.90 
Density per Unit 
2.79 
(0.4) 
2.78 
(0.4) 
-0.02 
2.88 
(0.4) 
2.86 
(0.4) 
-0.03 
English Proficiency 
11.71 
(10.2) 
11.73 
(10.2) 
0.02 
9.73 
(8.9) 
10.90 
(9.9) 
1.17 
Median Income ($ ’000) 
73.67 
(18.6) 
72.98 
(18.2) 
-0.69 
70.26 
(19.0) 
69.64 
(20.5) 
-0.61 
Share College Educated (%) 
56.33 
(8.9) 
56.30 
(8.8) 
-0.03 
53.91 
(8.9) 
53.85 
(9.9) 
-0.06 
Observations 
5,670 
3,471 
9,141 
542 
277 
819 
Panel B: Second Wave of Infections (08/01/2019-12/31/2019 vs. 08/01/2020-12/31/2020) 
Sale Price ($ ’000) 
769.41 
(517.7) 
806.35 
(754.2) 
36.94** 
647.07 
(381.2) 
740.55 
(483.4) 
93.48** 
# of Sales 
89.37 
(63.4) 
90.64 
(74.8) 
1.27 
9.26 
(6.4) 
8.79 
(9.2) 
-0.47 
Unemployment Rate (%) 
3.59 
(2.5) 
12.09 
(4.0) 
8.50*** 
4.41 
(2.9) 
12.59 
(3.7) 
8.18*** 
Renovated Home (yes==1) 
0.04 
(0.2) 
0.04 
(0.2) 
0.00 
0.02 
(0.1) 
0.03 
(0.2) 
0.01 
Building Area (’00 sqft) 
24.42 
(170.0) 
28.29 
(211.5) 
3.87 
32.48 
(258.6) 
29.69 
(243.7) 
-2.80 
Lot Area (’000 sqft) 
7.76 
(120.8) 
9.87 
(143.7) 
2.11 
12.05 
(166.3) 
13.50 
(190.8) 
1.45 
# of Floors 
2.03 
(0.5) 
2.02 
(0.5) 
-0.01 
2.00 
(0.5) 
2.02 
(0.5) 
0.02 
Basement (yes==1) 
1.00 
(0.0) 
1.00 
(0.0) 
0.00 
1.00 
(0.0) 
1.00 
(0.0) 
0.00 
Population (’000) 
54.49 
(25.6) 
52.82 
(25.2) 
-1.66*** 
53.94 
(24.5) 
54.13 
(24.1) 
0.19 
Share of White Residents (%) 
39.38 
(17.0) 
40.96 
(17.8) 
1.58*** 
40.09 
(18.6) 
43.97 
(19.2) 
3.89** 
Share of Rental Units(%) 
46.06 
(19.8) 
44.28 
(20.1) 
-1.78*** 
46.22 
(20.1) 
42.67 
(20.8) 
-3.55* 
Density per Unit 
2.79 
(0.4) 
2.76 
(0.4) 
-0.03*** 
2.86 
(0.4) 
2.79 
(0.4) 
-0.06* 
English Proficiency 
11.88 
(10.4) 
11.64 
(10.4) 
-0.24 
9.88 
(9.2) 
9.51 
(8.4) 
-0.37 
Median Income ($ ’000) 
73.64 
(18.4) 
75.43 
(18.8) 
1.79*** 
71.62 
(19.4) 
75.82 
(19.2) 
4.20** 
Share College Educated (%) 
56.39 
(8.6) 
57.00 
(8.8) 
0.62*** 
54.67 
(8.8) 
56.56 
(8.7) 
1.89** 
Observations 
6,345 
5,845 
12,190 
495 
354 
849 
Notes: Data on the number of one- and two-family property transactions and sale prices are published by the 
NYC Department of Finance. Information on NYC MODZCTA unemployment rates are published by DEEP-
MAPS. Statistics on housing characteristics are publicly available through the PLUTOTM database maintained by 
the NYC Department of City Planning. Neighborhood characteristics represent the sample averages for the 
respective samples of observed sales and are based on 2019 data available through American Community Survey 
published by the U.S. Census. Based on our sample, we define distinct post outbreak periods including the 1) the 
1st wave of infections from March 1, 2020 through July 31, 2020; and 2) 2nd wave of infections from August 1, 
2020 through December 31, 2020. For the purposes of comparison, we calculate pre-outbreak statistics based on 
a limited sample restricted to 1) March 1 to July 31, 2019; and 2) August 1 to December 31, 2019. 
 


===== PAGE 23 (score 0, numeric-paren 0, table-words 0, var-words 0) =====

23 
 
 
 
Figure 3: Cumulative Case Rates and MODZCTA Changes in the # of Property Sales and Avg. Sale 
Prices by Wave of Infection 


===== PAGE 25 (score 120, numeric-paren 101, table-words 5, var-words 9) =====

25 
Table 3: COVID-19 Case Effect on NYC One & Two Family Home Prices 
 
 
(1) 
(2) 
(3) 
(4) 
(5) 
(6) 
(7) 
(8) 
 
First Wave (03/01-07/31, 2003-2020) 
Second Wave (08/01-12/31, 2003-2020) 
 
Cum. Cases 
Cum. Cases Cum. 
Cases 
New 
Cases 
Cum. 
Cases 
Cum. Cases Cum. Cases 
New Cases 
Cases per 100,000 (’000) 
-0.382** 
-0.398** 
-0.706* 
10.162 
-0.611** 
-0.618*** 
-0.633*** 
-30.759*** 
(0.172) 
(0.174) 
(0.358) 
(12.208) 
(0.236) 
(0.235) 
(0.241) 
(7.905) 
Unemployment Rate (%) 
-0.064*** 
-0.039 
-0.039 
-0.072*** 
-0.060** 
-0.063*** 
(0.012) 
(0.024) 
(0.024) 
(0.013) 
(0.024) 
(0.024) 
Investor Owned (%) 
0.004 
0.004 
-0.009 
-0.009 
0.002 
0.002 
-0.011 
-0.010 
(0.009) 
(0.009) 
(0.013) 
(0.013) 
(0.010) 
(0.010) 
(0.014) 
(0.014) 
Altered (yes==1) 
1.356*** 
1.351*** 
2.432*** 
2.432*** 
1.997*** 
1.989*** 
3.056*** 
3.058*** 
(0.228) 
(0.228) 
(0.339) 
(0.340) 
(0.315) 
(0.315) 
(0.591) 
(0.591) 
Year Built 
0.002 
0.002 
0.003 
0.003 
(0.002) 
(0.002) 
(0.002) 
(0.002) 
Home Sqft (’000) 
2.174*** 
2.163*** 
2.174*** 
2.159*** 
(0.280) 
(0.281) 
(0.341) 
(0.341) 
Lot Sqft (’00,000) 
0.032** 
0.032** 
0.027* 
0.026* 
(0.014) 
(0.013) 
(0.015) 
(0.015) 
# of Floors 
0.051 
0.060 
0.045 
0.067 
(0.069) 
(0.071) 
(0.159) 
(0.158) 
Basement (yes==1) 
0.519*** 
0.502*** 
0.530*** 
0.506*** 
(0.109) 
(0.112) 
(0.133) 
(0.132) 
Constant 
-1.475 
-1.372 
0.790*** 
0.765*** 
-3.643 
-3.317 
0.753*** 
0.722*** 
(3.674) 
(3.633) 
(0.085) 
(0.083) 
(4.459) 
(4.413) 
(0.072) 
(0.069) 
N 
122,630 
121,980 
15,540 
15,540 
133,072 
132,393 
15,733 
15,733 
adj. R2 
0.606 
0.608 
0.146 
0.146 
0.551 
0.552 
0.131 
0.131 
F 
55.2*** 
47.4*** 
14.2*** 
13.4*** 
35.1*** 
32.3*** 
10.4*** 
11.5*** 
Month-of-sale FE 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Month-of-previous-sale FE 
N 
N 
Y 
Y 
N 
N 
Y 
Y 
MODZCTA FE 
Y 
Y 
N 
N 
Y 
Y 
N 
N 
Notes: Standard errors are reported in parenthesis. Across all specifications, we cluster standard errors at the 
MODZCTA level. The estimation sample underlying the results presented in columns (1) through (4) focuses on 
the pandemic-induced effects during first wave of infections and includes transactions of one or two-family 
homes sold in NYC between March and July for each year from 2003 through 2020, whereas the estimation 
sample underlying the results shown in columns (5) and (8) focus on the second wave and includes transactions 
of one or two-family homes sold in NYC between August and December for each year from 2003 through 2020. 
Estimation samples underlying the results presented in columns (1) and (2) as well as (5) and (6) include all sales 
of one or two-family home in NYC and each variable is given in levels, i.e. the dependent variable is given by 
the recorded sale price in $100,000. In contrast, estimation samples underlying the results presented in columns 
(3) and (4) as well as (7) and (8) are restricted to repeatedly-sold one or two-family properties. Accordingly, all 
variables are given in changes between sales, i.e. the dependent variable is given by the change in sale prices 
between sales in $100,000. Depending on the sample restriction and selection of control variables, these 
estimation samples include 149 to 160 MODZCTAs. The cumulative and new case rates are defined per 100,000 
MODZCTA residents. Across all estimations investor-owned properties are excluded. The variable “Investor 
Owned” controls for the share of investor-owned properties within a given home’s MODZCTA. Statistical 
significance at the conventional 10%, 5%, and 1% thresholds is given by *, **, ***, respectively. 
 
 
 
 
 


===== PAGE 26 (score 16, numeric-paren 0, table-words 3, var-words 10) =====

26 
positive effect on resale values. Other time-invariant property characteristics variables drop out when 
taking first differences for the repeat-sales estimations. 
Combined, the estimates reported in columns (1) through (8) suggest that larger COVID-19 case 
numbers occur in neighborhoods of lower-valued properties (see columns (1) and (2) as well as columns 
(5) and (6)) and that a worsening of the COVID-19 outbreak – especially during the second wave of 
infections – within any given neighborhood, when controlling for unemployment changes, is correlated 
with a reduction in the average value of repeat-sales properties within that MODZCTA (columns (3), (7) 
and (8)). These findings echo some of the recent reports on COVID-19, which have shown that the 
outbreak is more concentrated among poorer (i.e., unemployed) individuals (Goldstein, 2020), who are 
more likely to own or occupy lower-valued homes. 
5.2 Price Heterogeneity Analysis 
A priori, it is unclear whether these pandemic price effects are similar across price levels or how they may 
interact with buyer attributes and preferences. More affluent home buyers, for example, may be more 
sensitive to the possibility of contagion, while wealthier neighborhoods may be less likely to be impacted 
by contagion during the pandemic (i.e., less densely populated and more opportunity to isolate and work 
remotely). In the absence of observing buyer characteristics, we rely on pre-pandemic neighborhood 
attributes to test whether the contagion and income effects on home values vary with the characteristics 
of the residents living near the location of the sale. The pre-pandemic attributes are based on the 2019 
ACS and include population density per BBL, median neighborhood income, the share of rentals among 
all residential units, and the local population shares of residents using public transit, with limited English 
proficiency, having some college education, or who are foreign born. 
Table 4 provides the coefficient estimates for these interaction terms during the first wave of infections 
(Panel A) and the second wave of the pandemic (Panel B). Column (1) of Table 4 provides the baseline 
average effect estimates. In columns (2) and (3), we present price effect estimates for homes valued in the 
bottom and top 25th percentile of the observed local pre-pandemic price distribution, respectively. The 
results reveal an interesting pattern. While the contagion price discount is orders of magnitude larger for 


===== PAGE 28 (score 156, numeric-paren 132, table-words 7, var-words 10) =====

28 
Table 4: Heterogeneity of COVID-19 Case and Income Effects on Sales Prices of Repeatedly-sold Homes 
 
 
 
(1) 
(2) 
(3) 
(4) 
(5) 
(6) 
(7) 
(8) 
(9) 
(10) 
 
 
 
Base- 
Price 
Price 
Interaction w. Neighborhood Attributes 
 
 
 
line 
< 25th 
> 75th 
BBL 
Public 
Median 
College 
Rental 
Limited 
Foreign 
 
 
 
Results 
Perc. 
Perc. 
Density 
Transit 
Income 
Educated 
Share 
English 
Born 
Panel A: First Wave of Infections (03/01-07/31, 2003-2020) 
 
 
 
 
 
 
 
Cases per 100,000 (‘000) 
-0.706* 
-6.459*** 
-0.854* 
-0.848** 
-0.891** 
-0.591* 
-0.745** 
-0.972** 
-1.012*** 
-0.998*** 
 
 
 
(0.358) 
(1.364) 
(0.469) 
(0.396) 
(0.346) 
(0.339) 
(0.355) 
(0.407) 
(0.380) 
(0.365) 
Cases X Neigh. Attributes 
 
 
 
0.867*** 
0.739*** 
-0.693*** 
-0.348 
0.999*** 
0.531** 
0.501** 
 
 
 
 
 
 
(0.252) 
(0.247) 
(0.234) 
(0.212) 
(0.251) 
(0.241) 
(0.219) 
Unemployment Rate (%) 
-0.039 
-0.032 
0.019 
-0.005 
-0.020 
-0.053** 
-0.042* 
0.014 
-0.038 
-0.005 
 
 
 
(0.024) 
(0.027) 
(0.064) 
(0.025) 
(0.024) 
(0.026) 
(0.024) 
(0.026) 
(0.024) 
(0.032) 
Unemp. X Neigh. Attributes 
 
 
 
-0.080*** 
-0.064** 
0.044* 
0.008 
-0.086*** 
-0.015 
-0.055* 
 
 
 
 
 
 
(0.025) 
(0.028) 
(0.023) 
(0.027) 
(0.023) 
(0.025) 
(0.025) 
N 
 
 
15,540 
2,915 
4,360 
15,540 
15,540 
15,540 
15,540 
15,540 
15,540 
15,540 
adj. R2 
 
 
0.146 
0.168 
0.093 
0.147 
0.147 
0.147 
0.146 
0.148 
0.146 
0.146 
F 
 
 
14.2*** 
7.8*** 
16.3*** 
10.8*** 
11.3*** 
10.7*** 
10.6*** 
11.7*** 
10.3*** 
10.9*** 
 
 
 
 
 
 
 
 
 
 
 
 
 
Panel B: Second Wave of Infections (08/01-12/31, 2003-2020) 
 
 
 
 
 
 
 
Cases per 100,000 (‘000) 
-0.633*** 
0.200 
-1.242*** 
-0.694*** 
-0.806*** 
-0.549** 
-0.639** 
-0.701*** 
-0.712*** 
-0.677*** 
 
 
 
(0.241) 
(0.724) 
(0.353) 
(0.243) 
(0.236) 
(0.242) 
(0.246) 
(0.243) 
(0.232) 
(0.231) 
Case X Neigh. Attributes 
 
 
 
0.550*** 
0.636*** 
-0.276*** 
-0.095 
0.482*** 
0.253** 
0.204* 
 
 
 
 
 
 
(0.119) 
(0.127) 
(0.096) 
(0.105) 
(0.124) 
(0.109) 
(0.105) 
Unemployment Rate (%) 
-0.060** 
-0.005 
-0.070 
0.006 
-0.008 
-0.074*** 
-0.060** 
0.033 
-0.056** 
-0.044 
 
 
 
(0.024) 
(0.027) 
(0.053) 
(0.027) 
(0.025) 
(0.027) 
(0.024) 
(0.032) 
(0.024) 
(0.032) 
Unemp. X Neigh. Attributes 
 
 
 
-0.142*** 
-0.158*** 
0.046 
-0.011 
-0.145*** 
-0.031 
-0.030 
 
 
 
 
 
 
(0.028) 
(0.030) 
(0.029) 
(0.034) 
(0.030) 
(0.028) 
(0.031) 
N 
 
 
15,733 
3,208 
4,856 
15,733 
15,733 
15,733 
15,733 
15,733 
15,733 
15,733 
adj. R2 
 
 
0.131 
0.182 
0.070 
0.135 
0.135 
0.132 
0.131 
0.135 
0.131 
0.131 
F 
 
 
10.4*** 
0.2 
13.0*** 
11.1*** 
11.4*** 
8.7*** 
7.8*** 
9.9*** 
7.9*** 
8.2*** 
Month-of-sale FE 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Month-of-previous-sale FE 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
 
 
 
 
 
 
 
 
 
 
 
 
 
Notes: Standard errors are reported in parenthesis. Across all specifications, we cluster standard errors across MODZCTAs. Panel A shows the results focused on the pandemic-induced effects 
during first wave of infections between March and July for each year from 2003 through 2020, whereas Panel B shows results focused on the second wave between August and December for 
each year from 2003 through 2020. The estimation samples underlying the results are restricted to repeatedly-sold non-investor owned one or two-family properties. Accordingly, all variables 
are given in changes between sales, i.e., the dependent variable is given by the change in sale prices between sales in $100,000. Column (1) repeats the baseline results from Table 3. Columns 
(2) and (3) show the effects for homes priced in the bottom and top quartiles of pre-pandemic sale prices. Columns (4) through (11) show case and unemployment effects and their interactions 
with various neighborhood attributes. The interaction variables are indicators that equal 1 if a home is sold in a neighborhood with a above median characteristic. The indicators for ``BBL 
Density", for example, marks MODZCTAs with above median population per BBL. Indicators for ``Public Transit", ``Long Commute", ``Limited English", ``College Educated", and ``Foreign 
Born" represent MODZCTAs with above median population shares for residents that, for example, use public transit, have a commute longer than 30 minutes, are college educated, etc. 
Statistical significance at the conventional 10%, 5%, and 1% thresholds is given by *, **, ***, respectively. 
 
 


===== PAGE 29 (score 19, numeric-paren 0, table-words 6, var-words 7) =====

29 
 
case rates. The estimates given in columns (9) and (10) lend support to this hypothesis and show that the 
contagion effect is roughly half for properties located in these neighborhoods. 
Overall, these price effect dynamics across various neighborhood characteristics are similar during the 
first and second wave of the pandemic. Interestingly, the differences in contagion price effects are less 
pronounced during the second wave of the pandemic, while the differences in income effects intensify 
with in neighborhoods with above median density, public transit dependence and rental shares. 
5.3 Robustness Analysis 
We conduct a host of robustness analyses to test the sensitivity of our primary COVID-19 price effects 
(as initially reported in columns (3) and (7) of Table 3). We begin by adjusting the restrictions on our 
repeat-sales sample. The primary results are based on transactions of properties repeatedly sold either 
between March and July 2003-2020 (i.e., first infection wave) or between August and December 2003-
2020 (i.e., second wave of infections). In column (1) of Table 5, we report the COVID-19 and 
unemployment price effects when we relax these restrictions and estimate the average point estimates for 
the full sample instead. The coefficient estimates on case and unemployment rates are consistent in 
magnitude and are statistically significant at 1% level. 
Next, we consider the sensitivity of our result against the presence of influential outliers. One criticism 
against the use of repeat-sales samples has arisen from the literature of price indexes, which have been 
shown to be more sensitive to outliers when relying on a repeat-sales sample (see, for example, Wallace 
and Meese (1997)). Although we are not using the repeat-sales sample to construct a price index, we 
investigate the potential impact of outliers. Excluding transactions that are more or less than three standard 
deviations removed from the average change in sale prices between repeat sales reduces the number of 
observations by 187 transactions for the first-wave sample and 158 transactions for the second-wave 
sample. The coefficient estimates of interest remain stable and are statistically significant at the 
conventional levels (see column (2) of Table 5). 
Another potential concern with repeat sales is potential changes in unobservable property 
characteristics between sales. While the PLUTOTM database indicates whether a home has been altered 
from the perspective of the assessed value for taxation purposes, and we control for this alteration, it is 
possible that some properties undergo changes that are not necessarily captured by the tax authority and 


===== PAGE 31 (score 82, numeric-paren 69, table-words 5, var-words 3) =====

31 
Table 5: Robustness Tests of Repeat-Sales COVID-19 Price Effects 
(1) 
(2) 
(3) 
(4) 
(5) 
(6) 
(7) 
(8) 
Sample Restrictions 
Alternative Pandemic Measures 
All 
No 
Repeat 
Out- 
Sales 
liers 
> 3 yrs. 
between 
Sales 
# of 
Sales 
< 4 
NYC 
Shutdown 
30-Day Lag 60-Day Lag Borough 
Level 
Rates 
Panel A: First Wave of Infections (03/01-07/31, 2003-2020) 
Cases per 100,000 (’000) 
-0.594*** 
-0.669* 
-0.893 
-0.703* 
-0.719** 
-0.631* 
-0.422 
-1.005* 
(0.211) 
(0.351) 
(1.856) 
(0.359) 
(0.358) 
(0.344) 
(0.394) 
(0.580) 
Unemployment Rate (%) 
 
-0.053*** 
-0.068*** 
-0.074*** 
-0.041 
-0.039 
-0.037 
-0.043* 
-0.232* 
(0.018) 
(0.018) 
(0.026) 
(0.026) 
(0.024) 
(0.024) 
(0.023) 
(0.118) 
NYC Shutdown 
 
 
 
 
0.238 
 
 
 
 
 
 
 
(0.443) 
 
 
 
N 
36,051 
15,353 
6,174 
13,757 
15,540 
15,323 
15,094 
15,540 
adj. R2 
0.145 
0.228 
0.101 
0.136 
0.146 
0.146 
0.146 
0.146 
F 
19.7*** 
21.4*** 
11.2*** 
15.0*** 
14.1*** 
14.3*** 
13.1*** 
13.8*** 
Panel B: Second Wave of Infections (08/01-12/31,2003-2020) 
Cases per 100,000 (’000) 
-0.594*** 
-0.481** 
-1.060** 
-0.633*** 
- 
-0.622** 
-0.500 
-1.119*** 
 
(0.211) 
(0.198) 
(0.441) 
(0.242) 
- 
(0.297) 
(0.318) 
(0.344) 
Unemployment Rate (%) 
-0.053*** 
-0.070*** 
-0.064*** 
-0.064*** 
- 
-0.067*** 
-0.061*** 
-0.166 
 
(0.018) 
(0.019) 
(0.024) 
(0.024) 
- 
(0.022) 
(0.021) 
(0.105) 
N 
36,051 
15,575 
7,037 
14,014 
- 
15,570 
15,410 
15,733 
adj. R2 
0.145 
0.229 
0.107 
0.120 
- 
0.134 
0.134 
0.131 
F 
19.7*** 
21.5*** 
7.3*** 
10.1*** 
- 
10.2*** 
9.5*** 
8.8*** 
Home & Neigh. Charac. 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Month-of-Sale FE 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Month-of-Pre.-Sale FE 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Y 
Notes: Standard errors are reported in parenthesis. Across all specifications, we cluster standard errors across 
MODZCTAs. Panel A shows the results focused on the pandemic-induced effects during first wave of infections 
between March and July for each year from 2003 through 2020, whereas Panel B shows results focused on the 
second wave between August and December for each year from 2003 through 2020. Depending on the imposed 
sample restrictions, we observe sales in 135 to 155 MODZCTAs anywhere from as early as January 2003 through 
December 2020. Columns (1) through (4) show the COVID-19 cumulative case effect per 100,000 local 
MODZCTA residents (measured in 1,000 cases) as well as the unemployment effect on the change in sale prices 
between repeat transactions under varying sample restrictions. In column (5), we estimate the price effects of 
cumulative Coronavirus infections and unemployment while controlling for the signing of the New York State 
on PAUSE executive order. In columns (6) and (7), we report the 30-day and 60-day lagged effect of cumulative 
MODZCTA infection and unemployment rates. Finally, in column (8) we show the effect of changes in borough, 
rather than MODZCTA, cumulative infection and unemployment rates. Across all regressions, we control for 
time-varying home and neighborhood characteristics (i.e., whether a home was altered between sales and the 
share of investment-owned properties in a given neighborhood), month-of-sale and month-of-previous-sale fixed 
effects. Statistical significance at the conventional 10%, 5%, and 1% thresholds is given by *, **, ***, 
respectively. 
 
occurred after much of the activities in NYC had been restricted. If the shutdown of NYC, rather than the 
intensity of the local outbreak, is the explanation for the estimated price effect, then the inclusion of this 


===== PAGE 32 (score 8, numeric-paren 0, table-words 3, var-words 2) =====

32 
control variable should render our estimated case effect on home sale prices insignificant. If, however, we 
find that homes sold in neighborhoods with greater infection rates experience significantly larger price 
discounts even when controlling for the shutdown executive order, our estimate provides evidence of the 
contagion price effect (rather than the shutdown effect). Indeed, column (5) of Table 5 shows a statistically 
and economically significant price discount for homes located in neighborhoods with higher Coronavirus 
case rates, while the price effect of the shut-down is statistically insignificant.18 
Combined, these robustness tests based on various sample restrictions produce convincing evidence 
in support of our primary estimates of the pandemic-induced price effects and the proper identification 
thereof. A final set of robustness analyses considers the timing of the evidenced pandemic-induced price 
discount and sensitivity of our results against more aggregated measures of the intensity of the COVID-
19 pandemic and rise in unemployment. Similar to the sample restrictions, these tests produce estimates 
that reiterate and tend to support our primary findings. The results reported in columns (6) and (7) of 
Table 5, for example, show the estimated COVID-19 price discount with respect to 30- and 60-day lagged 
case and unemployment rates. While these lagged effects are quantitatively consistent with the primary 
results, the effects of 60-day lagged cumulative case rates are found to be statistically insignificant. Lastly, 
we consider case and unemployment rates at the more aggregated borough, rather than MODZCTA, level. 
Reassuringly, the estimates in column (8) of Table 5 show that our findings are generally robust against 
the extrapolation of MODZCTA case and unemployment rates. 
 
6 Discussion and Potential Limitations 
What is the economic significance of these estimates? Focusing on our repeat-sales sample results, our 
price estimate suggests that during the second wave of the pandemic the average sale price of a repeat-
sale NYC home drops by close to $63,000 for every 1,000 novel Coronavirus infections added to the 
cumulative case rate per 100,000 residents. Between August and December, 2020 (the end of our sample), 
the change in MODZCTA case rates in NYC ranged anywhere from 100 to 7,100 and averaged 1,776 
infections per 100,000 residents. Combining these statistics with our estimate suggests that the expected 
price effects may range from as little as $6,000 on the lower end to nearly $450,000 in neighborhoods 
with the largest increases in case rates and average around $100,000. A comparison of these figures to the 
 
18 We note that the gradual reopening was largely concluded by August 2020 and therefore we cannot estimate the effect of the 
NYC shut down during the second wave of the pandemic.