# RECORD 78763be000cac6c28c0629b4c2e5fffb2f7f7fde
# TITLE: Factors Affecting Spatial Autocorrelation in Residential Property Prices
# YEAR: 2022.0  VENUE: Land  DOI: 10.3390/land11060931
# AUTHORS: D. Lo; Wing-Chau Kwong; S. Wong; M. McCord; M. Haran
# PAGES: 17  CANDIDATE TABLE PAGES (1-based): [13]
# ABSTRACT: Within housing literature, the presence of spatial autocorrelation (S.A.) in housing prices is typically examined horizontally in a two-dimensional setting. However, in the context of apartment buildings, there is also a vertical component of S.A. for housing units located on different floor levels. This paper therefore explores the determinants of both horizontal and vertical S.A. within residential property prices. First, we posit that S.A. in housing prices is a consequence of the price discovery process of real estate, in which property traders acquire price information from recent market transactions (i.e., comparables) to value a subject property. Furthermore, we contend that the extent to which property traders rely on comparables to determine housing prices is governed by the liquidity and volatility conditions of the market, which in turn affects the magnitude of the S.A. By developing and testing several spatial autoregressive hedonic models using open market transaction data for the Hong Kong residential property market, we find that market liquidity tends to increase both vertical and horizontal S.A., whilst market volatility is more prone to increase vertical S.A. but depress horizontal S.A.


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Factors Affecting Spatial Autocorrelation in Residential Property Prices
Lo, D., Chau, K. W., Wong, S. K., McCord, M., & Haran, M. (2022). Factors Affecting Spatial Autocorrelation in
Residential Property Prices. Land, 11(6), 1-16. Article 931. https://doi.org/10.3390/land11060931
Link to publication record in Ulster University Research Portal
Published in:
Land
Publication Status:
Published (in print/issue): 17/06/2022
DOI:
10.3390/land11060931
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Download date: 20/10/2026


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Land 2022, 11, 931
12 of 16
prices. In comparison with the results of Equation (3), we observed no material difference
for the coefficients of the other hedonic variables.
Table 3. Regression Results.
Variables
Equation (2)
Equation (3)
Equation (7)
Equation (8)
Equation (9)
WVPt−m
-
0.0167
(0.0000)
0.0091
(0.0004)
0.0104
(0.0000)
0.0112
(0.0014)
WHPt−m
-
0.0184
(0.0000)
0.0141
(0.0000)
0.0264
(0.0000)
0.0230
(0.0000)
L ∗WVPt−m
-
-
1.74 × 10−5
(0.0001)
-
2.18 × 10−5
(0.0000)
L ∗WHPt−m
-
-
1.76 × 10−5
(0.0000)
-
1.54 × 10−5
(0.0000)
V ∗WVPt−m
-
-
-
0.3883
(0.0000)
0.4192
(0.0000)
V ∗WHPt−m
-
-
-
−0.1839
(0.0000)
−0.1877
(0.0000)
Constant
0.2045
(0.0000)
0.1677
(0.0000)
0.1704
(0.0000)
0.1675
(0.0000)
0.1698
(0.0000)
CBD
−6.57 × 10−5
(0.0000)
−5.98 × 10−5
(0.0000)
−5.80 × 10−5
(0.0000)
−5.96 × 10−5
(0.0000)
−5.78 × 10−5
(0.0000)
Age
−0.0305
(0.0000)
−0.0307
(0.0000)
−0.0307
(0.0000)
−0.0306
(0.0000)
−0.0306
(0.0000)
Age2
0.0003
(0.0000)
0.0003
(0.0000)
0.0003
(0.0000)
0.0003
(0.0000)
0.0003
(0.0000)
Area
0.0027
(0.0000)
0.0027
(0.0000)
0.0027
(0.0000)
0.0027
(0.0000)
0.0027
(0.0000)
Area2
−4.88 × 10−7
(0.0000)
−4.85 × 10−7
(0.0000)
−4.86 × 10−7
(0.0000)
−4.86 × 10−7
(0.0000)
−4.86 × 10−7
(0.0000)
Floor
0.0066
(0.0000)
0.0069
(0.0000)
0.0068
(0.0000)
0.0070
(0.0000)
0.0069
(0.0000)
Floor2
4.49 × 10−6
(0.0000)
1.67 × 10−6
(0.0000)
6.20 × 10−6
(0.0000)
3.05 × 10−6
(0.0000)
3.02 × 10−6
(0.0000)
Scale of Dev.
0.0014
(0.0000)
0.0014
(0.0000)
0.0014
(0.0000)
0.0014
(0.0000)
0.0014
(0.0000)
Scale of Dev.2
−1.59 × 10−6
(0.0000)
−1.59 × 10−6
(0.0000)
−1.60 × 10−6
(0.0000)
−1.59 × 10−6
(0.0000)
−1.60 × 10−6
(0.0000)
Included Obs
167,418
167,418
167,418
167,418
167,418
R2
0.896331
0.896594
0.896662
0.896638
0.896711
Adjusted R2
0.896169
0.896417
0.896483
0.896459
0.896531
Prob(F-Stat)
0.0000
0.0000
0.0000
0.0000
0.0000
Note: p-value in brackets.
Equation (7) expands the hedonic models further by including the liquidity factor (L)
in the spatial processes to test H(1) and H(2). As established above, a higher market
trading volume will increase vertical S.A., but the way in which it affects horizontal S.A.
is less apparent, depending on how traders make use of comparables. Our results show
that both θL,V and θL,H were positively signed and failed to reject H(2), but for H(1), the
results were significant at the 1% level. This finding infers that traders seem to employ
recently sold comparables found both within and outside the subject buildings to ascertain
new prices when the market is more liquid. This may also be a manifestation of the
relatively efficient nature of Hong Kong’s real estate market, where price information about
properties is easily accessible in terms of time and cost. The results for the coefficients
of other hedonic variables, as well as the overall explanatory power of the model, were
statistically satisfactory.
Equation (8), constructed to test H(3), differed from (6) in that the liquidity variable
was substituted by the volatility variable, V. The findings show that θV,V was positive
while θV,H was negative at the 1% level, in agreement with our expectation that when


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Land 2022, 11, 931
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the market is volatile, traders are inclined to put heavier weight on comparables found
in the same building, and less on other comparables; hence, H(3) was not rejected. The
results not only confirm the role volatility plays in the formation of S.A., but also are
suggestive that housing market participants react to varying levels of market uncertainty
when undertaking property valuation differently. The results for other hedonic variables
were statistically satisfactory in terms of the saliency and statistical significance of their
respective coefficients.
As a robustness test, Equation (9) takes into consideration the effects of both liquidity
and volatility in explaining the variation of S.A. in housing prices. The results confirm our
aforementioned information search conjectures in relation to the formation of S.A., which
is evidenced by all coefficients displaying the expected signs and significant at the 1% level.
Last but not least, we contend that since spatial autocorrelation in housing prices is not
time-varying, our empirical results should remain statistically intact even if a different
dataset of the Hong Kong housing market with a different time period of investigation
was examined.
5. Concluding Remarks
This paper attempts to explore the determinants of spatial autocorrelation in housing
prices, which is a much under-researched topic within the existing housing literature.
Throughout the paper, we argue that S.A. in housing prices is a direct consequence of the
price determination process undertaken by housing market participants. To verify this,
we proposed three hypotheses, linking (1) market liquidity and (2) market volatility with
S.A. in housing prices in a three-dimensional setting. Vertical S.A. is defined as spatial
autocorrelation in housing prices for housing units located in the same building, whereas
horizontal S.A. is defined as the counterpart for housing units located in different buildings.
With a huge number of open market transaction data for Hong Kong, we established and
statistically confirmed with the use of several spatial autoregressive hedonic models that
market liquidity tends to increase both vertical and horizontal S.A. We also found that
market volatility tends to increase vertical S.A. and depress horizontal S.A. Hence, we
can confirm the abovementioned role that price discovery plays in the formation of, and
changes in, the level of S.A. Moreover, our observation that S.A. is partially dependent
on liquidity and volatility may signal that the constant S.A. assumption, which is widely
accepted in the literature, should warrant closer scrutiny.
The findings of this paper reveal several implications beyond the theoretical housing
literature, particularly in the areas of property valuation and the economic analysis of the
housing market. Traditional hedonic models usually consist of a long list of locational
variables to account for the geographical factors of real estate. If inappropriately designed,
the models could likely produce spurious results. Furthermore, as elucidated by studies
such as McMillen and Redfearn, 2010 [64], McCord et al., 2014 [65], and Bidanset et al.,
2019 [66], geographic information systems allied with spatial–statistical approaches can
better model the spatial nature of property price data, which take into consideration spatial
dependence and spatial heterogeneity and help account for vertical and horizontal inequity.
Against this backdrop, a more thorough conceptualization on the causes of S.A. in housing
prices can prompt the development of more parsimonious yet more econometrically ap-
pealing spatial hedonic models, which can make the mass appraisal of properties much
less computationally intensive and more reliable.
Further research could be undertaken to examine whether the liquidity and volatility
arguments put forward in this study are empirically valid using data from other interna-
tional property markets. For instance, regional housing markets within the U.K. have, over
the course of the past few years, been extremely volatile, both in terms of price and return,
due to political uncertainty arising from political shocks. It would therefore be worth
investigating whether and how spatial autocorrelation in housing prices have changed
with respect to political events in the context of price determination. In addition, the recent
outbreak of COVID-19 has had a tremendous impact on almost every housing market across