# RECORD 877cbdabc2aba0920a744f15d91bf843fa770f5c
# TITLE: The Road to Higher Prices: Will Improved Road Standards Lead to Higher Housing Prices?
# YEAR: 2020.0  VENUE: Journal of real estate finance and economics  DOI: 10.1007/s11146-020-09751-y
# AUTHORS: Theis Theisen; A. Emblem
# PAGES: 25  CANDIDATE TABLE PAGES (1-based): [12, 13, 18, 19]
# ABSTRACT: We study the impact of improved road standards on housing prices in a region of Southern Norway, where towns are located along the coast, like pearls on a string, with sparsely populated areas between the towns. In this region towns have since long been linked together by a road, but in 2009 a new highway, roughly parallel to the old road, was opened. The new highway runs Eastwards from the main town in the region and reduced one-way commutes to the main town by 10–15 min. We examine how this change in transportation infrastructure has affected housing prices, using difference-in-difference regressions to identify the impact of the new highway. Three smaller towns located East of the main town constitute the treatment region, while four towns and communities West of the main town form the control region. We find that the new highway on average increased housing prices in the treatment region by about 5%, but the effect on price differed substantially between the towns. The full impact on housing prices materialized when the new highway was opened. We estimate that the increase in total housing capital in the treatment region amounts to almost 84% of the construction costs of the new highway.


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The Road to Higher Prices: Will Improved Road
Standards Lead to Higher Housing Prices?
Theis Theisen1
& Anne Wenche Emblem1
Published online: 14 March 2020
Abstract
We study the impact of improved road standards on housing prices in a region of Southern
Norway, where towns are located along the coast, like pearls on a string, with sparsely
populated areas between the towns. In this region towns have since long been linked
together by a road, but in 2009 a new highway, roughly parallel to the old road, was opened.
The new highway runs Eastwards from the main town in the region and reduced one-way
commutes to the main town by 10–15 min. We examine how this change in transportation
infrastructure has affected housing prices, using difference-in-difference regressions to
identify the impact of the new highway. Three smaller towns located East of the main town
constitute the treatment region, while four towns and communities West of the main town
form the control region. We find that the new highway on average increased housing prices
in the treatment region by about 5%, but the effect on price differed substantially between the
towns. The full impact on housing prices materialized when the new highway was opened.
We estimate that the increase in total housing capital in the treatment region amounts to
almost 84% of the construction costs of the new highway.
Keywords Roadinvestments.Infrastructureinvestments.Housingprices.Difference-in-
difference
Introduction
Since the contributions of Mohring (1961), Alonso (1964), Muth (1969), Mills (1972),
Solow (1972) and others, the relationship between housing (land) prices and monetary
J Real Estate Finan Econ (2021) 62:258–282
https://doi.org/10.1007/s11146-020-09751-y
* Theis Theisen
Theis.Theisen@uia.no
Anne Wenche Emblem
Anne.Wenche.Emblem@uia.no
1
Department of Economics and Finance, School of Business and Law, University of Agder, P.O.
box 422, 4604 Kristiansand, Norway
# The Author(s) 2020


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Table 2 Descriptive statistics, entire sample, N = 37,216
Mean
Std. Dev.
Min
Max
Dependent variables:
Price
1,966,910
1,053,614
40,000
23,000,000
Mutual
46,671.8
187,298.3
0
3,440,000
Dwelling characteristics and lot:
Floor
111.3869
52.51462
16
467
Age
36.89255
35.75254
0
461
Lota
402.2755
5104.214
0
381,354
Dwelling types and ownership
Co-op
.1649559
.3711458
0
1
Detached
.3908534
.4879482
0
1
Twin-house
.1018379
.3024391
0
1
Row-house
.0940993
.2919708
0
1
Apartment
.4132094
.4924164
0
1
Treatment variables
TreatA
.036624
.1878395
0
1
TreatB
.082572
.2752379
0
1
TreatC
.1793046
.3836123
0
1
TreatE
.2985006
.4576064
0
1
TreatK
.5382094
.4985446
0
1
a. Lot size above 1000 sqm. For non-cooperative dwellings exclusive of apartments
Table 3 Descriptive statistics for subsamples
Mean values
P-values for test of different meansc
Variable
Treatment
Control
Core
Treatment
Treatment
Control
region
region
town
Control
Core
Core
Price
1,828,969
1,810,550
2,090,853
0.2107
0.0000***
0.0000***
Mutuala
402,929.9
921,140.5
238,264.2
0.0000***
0.0000***
0.0000***
Floor
119.9799
122.7199
103.1826
0.0008***
0.0000***
0.0000***
Age
40.51661
29.7321
37.05502
0.0008***
0.0000***
0.0000***
Lotb
693.9936
466.2936
198.3691
0.0339*
0.0061**
0.0022**
Co-op
.084436
.026987
.2514728
0.0000***
0.0000***
0.0000***
Detached
.5859213
.4999177
.2495756
0.0000***
0.0000***
0.0000***
Twin-house
.0864164
.1316439
.101348
0.0000***
0.0000***
0.0000***
Row-house
.0559006
.0929735
.1156266
0.0000***
0.0000***
0.0000***
Apartment
.2717616
.2754649
.5334498
0.6024
0.0000***
0.0000***
N
11,109
6077
20,030
a. means of mutual debt for dwellings that carry a positive mutual debt
b. Lot size above 1000 sqm. For non-cooperative dwellings exclusive of apartments
c. Stars indicate statistical significance, with p-values: * p < 0.05, ** p < 0.01, *** p < 0.001
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Table 4 Correlation matrix with P-values in parentheses, N = 37,216a
Treat E10
Floor
Age
Lot
Co-op
Twin- house
Row-house
Apartment
TreatE10
1.0000
–
Floor
.0499***
1.0000
(.0000)
–
Age
−.0379***
.0194***
1.0000
(.0000)
(.0002)
–
Lot
.0265***
.1049***
.0786***
1.0000
(.0000)
(.0000)
(.0000)
–
Co-op
−.0748***
−.3407***
−.0247***
−.0665***
1.0000
(.0000)
(.0000)
(.0000)
(.0000)
–
Twin-house
−.0212***
.0156**
−.0609***
−.0008
−.1348***
1.0000
(.0000)
(.0026)
(.0000)
(.8698)
(.0000)
–
Row-house
−.0536***
−.0190***
−.0323***
−.0209***
−.0304***
−.1085***
1.0000
(.0000)
(.0003)
(.0000)
(.0001)
(.0000)
(.0000)
–
Apartment
−.0775***
−.6391***
−.1070***
−.1256***
.4453***
−.2826***
−2705***
1.0000
(.0000)
(.0000)
(.0000)
(.0000)
(.0000)
(.0000)
(.0000)
–
a. Stars indicate statistical significance, with p-values: * p < 0.05, ** p < 0.01, *** p < 0.001
270
T. Theisen, A. W. Emblem


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correlation between most independent variables is low. The only exception is the
correlation between the apartment dummy and floor-space, but even this is not alarm-
ingly high.
Figure 3 shows box plots of housing price per square meter floor-space for detached
dwellings, which dominates the housing market in both the treatment and control
regions, and of which there are many also in the Core town. The figure reveals that
the median price as well as the change in prices over the entire study period is similar
for the treatment region and the control region. The Core town differs, however, in that
the price level is higher, and the price increase over the entire period is stronger than in
the treatment and control region. The higher price level in the Core town may inter alia
be related to the fact that its population is larger than in the other towns and commu-
nities. Stronger price growth in the Core town may be related to stronger population
growth in the Core town than in the treatment and control regions, cf. Fig. 2. The higher
housing price level and growth in the Core town are among the reasons why we have
chosen to exclude this town from both the treatment and the control region.
Empirical Results
Results from estimating various versions of Eq. (4) are shown in Table 5. In Regression
A all towns in the treatment region are subsumed under one category, TreatE. The
treatment period includes the years 2010–2013, the years 2003–2006 constitute the
non-treatment period, while data from the years 2007–2009 are excluded from the
estimation. Regression A explains 71% of the variation in housing prices. The Vif
statistics indicate that collinearity of regressors is not a problem, but since the Breusch-
Pagan statistics reveals that error terms are heteroscedastic we use throughout robust
standard errors estimated with Huber-White’s “sandwich” estimator.
The treatment effect in Regression A is affiliated with the variable TreatE10, where
E indicates that the treatment region lies East of the Core town and 10 indicates that the
first year in the treatment period, i.e. the first year when the full treatment effect kicks
in, is 2010. The impact parameter of TreatE10 in Table 5 is statistically significant and
amounts to 0.0488. Hence, we ascribe an increase in housing prices in the treatment
region of almost 5% to the new highway.6 Except for the Apartment dummy, all other
independent variables in Regression A that are explicitly shown in Table 5 have a
statistically significant impact on housing prices, with signs and magnitudes that are
reasonable and in good accordance with previous research. In estimating Regression A
we have controlled for the location of dwellings by including zip-codes. Many of these
have a strong and statistically significant impact on housing prices, but since they are
not of primary interest for the issues we address, they are not shown in Table 5. Dummy
6 We estimated also a variant of Regression A in which all terms entered linearly, as well as a semilogarithmic
function where the dependent variable was log-transformed while all independent variables entered untrans-
formed. In contrast to the double-logarithmic form, the residuals in the two other functional forms deviated
substantially from the normal distribution, and heteroskedasticity was also much more pronounced than in the
double-logarithmic form we have used throughout the paper. Hence, we do not pay more attention to the linear
and semilogarithmic functional forms, but to mention that in the semilogarithmic function the treatment
variable, TreatE10, indicated an increase in housing price by 5.4%, while the linear function implied that
treatment increased housing prices by 8.4%.
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Regression A, while Rob-2 shows results from estimation Regression A using
bζ ¼ 0:70. Comparing Rob-1 and Rob-2 with Regression-A it is evident that the
estimated treatment parameter is robust towards even very large variations in the
mutual debt parameter. Moreover, except for the parameter affiliated with the Co-op-
variable the other coefficients in Rob-1 and Rob-2, including the treatment coefficients,
are very similar to the corresponding coefficients in Regression A. The fit of Regres-
sion A is also slightly better than that for Rob-1, and very similar to that of Rob-2.
Hence, we conclude that using bζ ¼ 0:86 as prior information in the estimation seems to
be unproblematic.
Rob-3 in Table 7 shows results from a placebo analysis, where the treatment effect is
assumed to materialize as early as in 2004, i.e. one year before the decision to build the
new highway was made. The estimated treatment parameter is close to zero and
statistically insignificant, indicating that housing prices in the treatment region did
not divert from those in the control region as early as in the year prior to the final
decision to build the new highway.
Table 6 Alternative starts of treatment period, OLS regressions, N = 17,186a
Year treatment period is assumed to start
2005
2006
2007
2008
2009
2010
Treatment coefficient
.0032
.0140
.0262**
.0398***
.0539***
.0549***
(.0110)
(.0096)
(.0084)
(.0079)
(.0076)
(.0076)
Ln (Floor)
.5845***
.5848***
.5850***
.5853***
.5853***
.5852***
(.0072)
(.0072)
(.0072)
(.0072)
(.0072)
(.0072)
Ln (Age)
−.0629***
−.0629***
−.0629***
−.0628***
−.0628***
−.0627***
(.0020)
(.0020)
(.0020)
(.0020)
(.0020)
(.0020)
Ln (Lot)
.0094***
.0094***
.0094***
.0094***
.0094***
.0094***
(.0011)
(.0011)
(.0011)
(.0011)
(.0011)
(.0011)
Co-op
−.0423***
−.0422***
−.0419***
−.0417***
−.0419***
−.0418***
(.0093)
(.0093)
(.0093)
(.0093)
(.0093)
(.0093)
Twin-house
−.0695***
−.0693***
−.0692***
−.0690***
−.0693***
−.0693***
(.0058)
(.0058)
(.0058)
(.0058)
(.0058)
(.0058)
Row-house
−.1131***
−.1131***
−.1129***
−.1128***
−.1126***
−.1128***
(.0064)
(.0064)
(.0064)
(.0064)
(.0064)
(.0064)
Apartment
.0031
.0032
.0032
.0032
.0029
.0034
(.0073)
(.0073)
(.0073)
(.0073)
(.0073)
(.0073)
Constant
11.53***
11.52***
11.52***
11.52***
11.51***
11.52***
(.0397)
(.0395)
(.0396)
(.0396)
(.0395)
(.0395)
Zip-codes
Yes
Yes
Yes
Yes
Yes
Yes
Years
Yes
Yes
Yes
Yes
Yes
Yes
Months
Yes
Yes
Yes
Yes
Yes
Yes
R-Sq
.6901
.6901
.6902
.6905
.6909
.6909
a. Dependent variable: Ln (Price+0.86*MUT). Robust (Huber-White) standard errors in parentheses. Stars
indicate statistical significance, with * denoting 5% level, ** 1% level and *** 0.1% level
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Table 7 Robustness checks based on Regression A, OLSa
Regression A
Rob-1
Rob-2
Rob-3
Rob-4
Rob-5
Rob-6
ζ = 0.86
ζ = 1.0
ζ = 0.7
TreatE04
Detached
Small
Apartment
TreatE10
.0488***
.0496***
.0478***
.0446***
.0658***
.0391***
(.0092)
(.0092)
(.0092)
(.0138)
(.0150)
(.0173)
TreatE04
−.0078
(.0144)
Ln(Floor)
.5825***
.5820***
.5832***
.5844***
.5716***
.4651***
.6615***
(.0086)
(.0086)
(.0086)
(.0072)
(.0119)
(.0167)
(.0162)
Ln(Age)
−.0679***
−.0690***
−.0664***
−.0630***
−.0625***
−.0661***
−.0786***
(.0023)
(.0023)
(.0023)
(.0020)
(.0041)
(.0045)
(.0035)
Ln(Lot)
.0097***
.0098***
.0097***
.0094***
.0103***
.0016***
(.0013)
(.0013)
.0013)
(.0011)
(.0014)
(.0023)
Co-op
−.0382***
.0032
−.0896***
−.0423***
.0369***
−.0497***
(.0107)
(.0107)
(.0117)
(.0093)
(.0552)
(.0140)
Twin-
−.0740***
−.0752***
−.0725***
−.0695***
.0499***
house
(.0069)
(.0069)
(.0069)
(.0058)
(.0082)
Row-
−.1168***
−.1168***
−.1167***
−.1131***
house
(.0074)
(.0074)
(.0075)
(.0064)
Apartment
.0017
−.0006
.0048
.0031
(.0086)
(.0086)
(.0086)
(.0073)
Constant
11.54***
11.55***
11.53***
11.53***
11.63***
11.97***
11.02***
(.0473)
(.0472)
(.0474)
(.0399)
(.0712)
(.1243)
(.0745)
R-Sq
.7100
.7095
.7101
.6901
.6632
.7848
.7386
N
12,630
12,630
12,630
17,186
7053
2122
3455
a. Dependent variable: Ln(Price+ζ*MUT), with ζ = 0.86, except in Rob1 and Rob2, where ζ is specified in the head of the table. Rob1 and Rob2 are variants of Regression A. In Rob-3
the treatment effect is assumed to kick in in 2004. Rob-4, Rob-5 and Rob-6 are similar to Regression A, but estimated from the subsamples of respectively detached houses, small
houses and apartments. Robust (Huber-White) standard errors in parentheses. Stars indicate statistical significance, with * denoting 5% level, ** 1% level and *** 0.1% level
276
T. Theisen, A. W. Emblem


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In regressions Rob-4, Rob-5 and Rob-6 in Table 7 we have re-estimated Regression
A separately for detached houses, apartments, and small houses (row-houses and twin-
houses taken together). The estimated treatment parameters for the three types of
dwellings are all positive and statistically significantly different from zero. The
dwelling-type specific parameter estimates are not statistically significantly different
from the treatment parameter in Regression A, but the differences are sufficiently large
to warrant a closer examination.
In Table 8 we present results from estimating Regression A separately for three types
of dwellings, with alternative assumptions on when the treatment effect kicks in.
Hence, the estimated relationships are of the same type as those in Table 6, except that
they are dwelling-type specific in Table 8. We observe from Table 8 that the estimated
treatment coefficients for each of the three dwelling types are roughly of the same
magnitude for the years 2009 and 2010, while the estimates for earlier years are smaller.
Hence, for each of the three dwelling types, it seems that the full treatment effect
materializes towards the end of the construction period. Since the estimated coefficients
for detached houses, small houses and apartments are far from being statistically
significantly different from each other, this provides evidence that the full treatment
effect is similar for all types of dwellings.
Consider now the results obtained by assuming that the treatment effect kicks in in
alternative years before 2009. The regressions in column 2008 in Table 8 are based on
the presumption that the treatment effect kicks in in 2008, and show that the magnitude
of the estimated treatment coefficient is much larger for small houses and apartments
than for detached houses. Moreover, for small houses and apartments the estimated
treatment effects in column 2008 are almost as large as those in columns 2009 and
2010. Hence, from the results in Table 8 it seems warranted to conclude that the
treatment effect for apartments and small houses kicks in with considerable magnitude
already in the middle of the construction period. For detached houses by contrast, the
estimates in Table 8 indicate that the treatment effect in the middle of the treatment
period is much weaker. Finally, from the results in Tables 8 and 6 there are indications
of a small treatment effect also in 2007, the year when construction of the new road
started up. Hence, we conclude that the treatment effect seems to kick in somewhat
earlier for apartments and small houses, and that the early effects also seem to be
stronger for such dwellings than for detached houses.
The observations just made deserve some elaboration. An important reason why the
treatment effect does not kick in before the year construction of the new road started
may be that people do not react before they with their own eyes see that construction of
the new road has started. Next, there may be several reasons why the treatment effect in
the middle of the construction period according to our results seems to be stronger for
small houses and apartments: First, most apartments and small houses are more
standardized than detached houses, i.e. comparable apartments and small houses are
easily found. Market actors may then easily find comparable units recently sold.
Adjustment of asking price to a realistic level may then work smoother than for
detached houses, where it may be hard to find similar houses recently sold within the
same local environment. In addition, sellers of detached houses in the markets we
consider often may have to wait quite long before the “right” bidder turns up and hands
in his or her bid. Consequently, sellers of detached houses may often be uncertain why
he or she does not see a “sufficiently high” bid: Is it because the asking price is too high
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