# RECORD 0fe82e8b18cc371498773e34654f4904c5d797a9
# TITLE: Homebuyer Purchase Decisions: Are They Anchoring to Appraisal Values or Market Prices?
# YEAR: 2022.0  VENUE: Journal of Risk and Financial Management  DOI: 10.3390/jrfm15040159
# AUTHORS: Ka-Shing Cheung; Chung Yim Edward Yiu; Yihan Guan
# PAGES: 14  CANDIDATE TABLE PAGES (1-based): [9]
# ABSTRACT: Price discovery is an important research topic in real estate due to the heterogeneous nature of housing attributes and relatively thin trading activities compared to other assets. In Commonwealth countries, including New Zealand, governments usually conduct periodic appraisals for the purpose of collecting rates and levies. Such official appraisal values of properties, also known as capital values (CVs), are considered a price anchor for market participants in their negotiation processes. Real estate agents often use these appraisal values to advertise their listings and negotiate transaction prices. In this study, we aim to make an initial attempt to study the influence of CV on market prices using Granger causality tests and a hedonic pricing model. To test the lead-lag relationships, three million housing transactions from 1990 to 2020 in New Zealand are used to construct the capital values (CVs) and transacted prices (TPs) indices in both primary and secondary housing markets. The Granger causality test suggests that the indices of TPs and CVs have a bi-directional lead-lag relationship in the secondary housing market, whereas the relationship does not follow in the primary market where the information on CVs is unavailable. The results imply the existence of a CV anchoring effect. Such anchoring effects are also contingent on the timeliness of price anchors, which is consistent with the availability heuristic from behavioural economics.


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Cheung, Ka Shing; Yiu, Chung Yim; Guan, Yihan
Article
Homebuyer purchase decisions: Are they anchoring to
appraisal values or market prices?
Journal of Risk and Financial Management
Provided in Cooperation with:
MDPI – Multidisciplinary Digital Publishing Institute, Basel
Suggested Citation: Cheung, Ka Shing; Yiu, Chung Yim; Guan, Yihan (2022) : Homebuyer purchase
decisions: Are they anchoring to appraisal values or market prices?, Journal of Risk and Financial
Management, ISSN 1911-8074, MDPI, Basel, Vol. 15, Iss. 4, pp. 1-13,
https://doi.org/10.3390/jrfm15040159
This Version is available at:
https://hdl.handle.net/10419/258882
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J. Risk Financial Manag. 2022, 15, 159
8 of 13
Figure 1. House Price Indices of CV and TP in the primary and secondary house markets of New
Zealand, 1990–2020 (Index = 100 in 1990). Notes: The four house price indices of New Zealand from
1990 to 2020 are (TP1) hedonic house prices index in the primary market; (CV2) capital values index
in the secondary market; and (TP2) hedonic house price index in the secondary market. For the
capital value index of the primary market (CV1), we use the estimated CV typically released by the
government after the housing transaction.
The descriptive statistics for the data are shown in Table 1. For the house price index
of the first-hand market (TP1), the mean of TP1 is 3.46, whereas the mean of TP1 from 1990
to 2009 is 2.26. The mean of TP1 from 2009 to 2020 is 5.63, which increased by 149% after
the Global Financial Crisis (GFC). For the house price index of the second-hand market,
from 1990 to 2009, the index was 1.83, whereas from 2009 to 2020, it was 4.23. The CV in
the first-hand market is 2.08 for the pre-GFC period and 5.34 for the post-GFC period, and
the CV in the second-hand market is 1.78 for pre-GFC and 4.11 for post-GFC. Even though
the GFC “raided” the market, the change of TP and CV still kept the same pace.
Table 1. Descriptive statistics of the four house submarkets price indices.
(1)
(2)
(3)
(4)
(5)
Variable
Mean (Whole
Period)
Mean
(1990–2009)
Mean
(2009–2020)
Changes in
Two Periods
Standard
Deviation
(Whole Period)
TP1
3.46
2.26
5.63
149%
1.95
TP2
2.67
1.83
4.23
131%
1.41
CV1
3.24
2.08
5.34
156%
1.84
CV2
2.61
1.78
4.11
130%
1.34
Notes: The mean of each variable refers to the mean of the monthly house price index of the variable. Column (1)
refers to the mean of the whole data period, whereas Columns (2) and (3) are the means of the pre- and post-GFC
periods, respectively. Column (4) refers to the change of the means from the pre-GFC period to the post-GFC
period. Column (5) shows the standard deviation of the monthly house price index of the whole period variable.
The descriptive statistics for the data in the hedonic models are stated below: the mean
of the transaction price is NZD323256.8, whereas for the CV, the mean is NZD288902.8. In
a booming property market, the earlier appraised value should be lower than the price
that occurs later. The mean number of bedrooms in the dataset is 3.126, whereas the mean
number of bathrooms is 1.398. The average floor area is 125.708 sm. The time dummy
variable is monthly data from 1990M01 to 2020M12. The regional dummy variable includes
14 regions, and the View dummy contains three types of view: ‘No appreciable view’, ‘focal
point of water view’, and ‘focal point of other views’.


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J. Risk Financial Manag. 2022, 15, 159
9 of 13
Empirical Results
In order to conduct the Granger causality test between two time-series, we need to
ensure the data is stationary by making sure that none of our datasets has any unit root.
To test the stationarity of the variables, we use the Augmented Dickey–Fuller test and
the Kwiatkowski–Phillips–Schmidt–Shin test. The results are shown in Table 2. All the
variables are not stationary at I (0), whereas at I (1), all the time series variables show as
stationary. Therefore, we need to use first differenced data for all the time series variables
and follow the modified Granger causality processes to conduct the test.
Table 2. The result of the Unit root test.
Variable
Incl. Trend?
ADF Test
KPSS Test
Order of Integration
ln(TP1)
Without trend
−0.6319
2.3861
With trend
−1.7342
0.2268
dln(TP1)
Without trend
−17.5034
0.0802
I (1)
With trend
−17.4858
0.0688
I (1)
ln(TP2)
Without trend
0.4198
2.3972
With trend
−2.8185
0.1331
dln(TP2)
Without trend
−3.1530
0.2189
I (1)
With trend
−3.2187
0.1141
I (1)
ln(CV1)
Without trend
−0.8090
2.3955
With trend
−1.4885
0.1984
dln(CV1)
Without trend
−14.8398
0.0976
I (1)
With trend
−14.8371
0.0826
I (1)
ln(CV2)
Without trend
−0.3984
2.3851
With trend
−3.7292
0.2163
dln(CV2)
Without trend
−3.2861
0.1088
I (1)
With trend
−3.3001
0.0790
I (1)
Notes: The 5% critical values of the ADF and KPSS test statistics are, respectively, 2.87 and 0.463 (without trend)
and 3.42 and 0.146 (with trend). A significant test statistic indicates the rejection of the null hypothesis, which is
non-stationarity in the ADF tests and stationarity in the KPSS test.
Using the AIC test, we are able to determine the lag periods. The AIC test results for
the lag length used for the Granger causality test between the natural logarithm of the
first-hand TP and the natural logarithm of the first-hand CV are shown in Table 3. The
optimal lag length p is 2 (months) after first differencing dln(TP1) and dln(CV1), whereas
the maximum order of integration m is 1. Then, we use p + m, which is 3 (months), as
the lag length between dln(TP1) and dln(CV1). At the same time, the result between the
second-hand TP and the second-hand CV after first differencing dln(TP2) and dln(CV2)
shows that the optimal lag length p is 12 (months). By adding the maximum order of
integration (m) 1, the lag length we use for the Granger causality test between dln(TP2) and
dln(CV2) is 13 (months).
Table 3. Result of AIC tests.
Variable
Lag Length
AIC Result
dln(TP1)/dln(CV1)
2
−11.91756
dln(TP2)/dln(CV2)
12
−15.54021
Table 4 shows the results of Granger causality tests, which in the first-hand market,
the TP does not Granger-cause the CV, nor does the CV Granger-cause the TP while in
the second-hand market. In the secondary market, the null hypothesis of “dln(TP2) does
not Granger-cause dln(CV2)” is rejected at 5 × 10−5 the p-value, and the null hypothesis
of “dln(CV2) does not Granger cause dln(TP2)” is also rejected at 2 × 10−8 the p-value.
This indicates that the result shows that CV Granger-causes TP and vice versa in the
second-hand market.