1# RECORD 0fe82e8b18cc371498773e34654f4904c5d797a92# TITLE: Homebuyer Purchase Decisions: Are They Anchoring to Appraisal Values or Market Prices?3# YEAR: 2022.0 VENUE: Journal of Risk and Financial Management DOI: 10.3390/jrfm150401594# AUTHORS: Ka-Shing Cheung; Chung Yim Edward Yiu; Yihan Guan5# PAGES: 14 CANDIDATE TABLE PAGES (1-based): [9]6# 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.789===== PAGE 1 (score 1, numeric-paren 0, table-words 0, var-words 1) =====1011Cheung, Ka Shing; Yiu, Chung Yim; Guan, Yihan12Article13Homebuyer purchase decisions: Are they anchoring to14appraisal values or market prices?15Journal of Risk and Financial Management16Provided in Cooperation with:17MDPI – Multidisciplinary Digital Publishing Institute, Basel18Suggested Citation: Cheung, Ka Shing; Yiu, Chung Yim; Guan, Yihan (2022) : Homebuyer purchase19decisions: Are they anchoring to appraisal values or market prices?, Journal of Risk and Financial20Management, ISSN 1911-8074, MDPI, Basel, Vol. 15, Iss. 4, pp. 1-13,21https://doi.org/10.3390/jrfm1504015922This Version is available at:23https://hdl.handle.net/10419/25888224Standard-Nutzungsbedingungen:25Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen26Zwecken und zum Privatgebrauch gespeichert und kopiert werden.27Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle28Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich29machen, vertreiben oder anderweitig nutzen.30Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen31(insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten,32gelten abweichend von diesen Nutzungsbedingungen die in der dort33genannten Lizenz gewährten Nutzungsrechte.34Terms of use:35Documents in EconStor may be saved and copied for your personal36and scholarly purposes.37You are not to copy documents for public or commercial purposes, to38exhibit the documents publicly, to make them publicly available on the39internet, or to distribute or otherwise use the documents in public.40If the documents have been made available under an Open Content41Licence (especially Creative Commons Licences), you may exercise42further usage rights as specified in the indicated licence.43 https://creativecommons.org/licenses/by/4.0/444546===== PAGE 9 (score 14, numeric-paren 0, table-words 2, var-words 8) =====4748J. Risk Financial Manag. 2022, 15, 159498 of 1350Figure 1. House Price Indices of CV and TP in the primary and secondary house markets of New51Zealand, 1990–2020 (Index = 100 in 1990). Notes: The four house price indices of New Zealand from521990 to 2020 are (TP1) hedonic house prices index in the primary market; (CV2) capital values index53in the secondary market; and (TP2) hedonic house price index in the secondary market. For the54capital value index of the primary market (CV1), we use the estimated CV typically released by the55government after the housing transaction.56The descriptive statistics for the data are shown in Table 1. For the house price index57of the first-hand market (TP1), the mean of TP1 is 3.46, whereas the mean of TP1 from 199058to 2009 is 2.26. The mean of TP1 from 2009 to 2020 is 5.63, which increased by 149% after59the Global Financial Crisis (GFC). For the house price index of the second-hand market,60from 1990 to 2009, the index was 1.83, whereas from 2009 to 2020, it was 4.23. The CV in61the first-hand market is 2.08 for the pre-GFC period and 5.34 for the post-GFC period, and62the CV in the second-hand market is 1.78 for pre-GFC and 4.11 for post-GFC. Even though63the GFC “raided” the market, the change of TP and CV still kept the same pace.64Table 1. Descriptive statistics of the four house submarkets price indices.65(1)66(2)67(3)68(4)69(5)70Variable71Mean (Whole72Period)73Mean74(1990–2009)75Mean76(2009–2020)77Changes in78Two Periods79Standard80Deviation81(Whole Period)82TP1833.46842.26855.6386149%871.9588TP2892.67901.83914.2392131%931.4194CV1953.24962.08975.3498156%991.84100CV21012.611021.781034.11104130%1051.34106Notes: The mean of each variable refers to the mean of the monthly house price index of the variable. Column (1)107refers to the mean of the whole data period, whereas Columns (2) and (3) are the means of the pre- and post-GFC108periods, respectively. Column (4) refers to the change of the means from the pre-GFC period to the post-GFC109period. Column (5) shows the standard deviation of the monthly house price index of the whole period variable.110The descriptive statistics for the data in the hedonic models are stated below: the mean111of the transaction price is NZD323256.8, whereas for the CV, the mean is NZD288902.8. In112a booming property market, the earlier appraised value should be lower than the price113that occurs later. The mean number of bedrooms in the dataset is 3.126, whereas the mean114number of bathrooms is 1.398. The average floor area is 125.708 sm. The time dummy115variable is monthly data from 1990M01 to 2020M12. The regional dummy variable includes11614 regions, and the View dummy contains three types of view: ‘No appreciable view’, ‘focal117point of water view’, and ‘focal point of other views’.118119120===== PAGE 10 (score 26, numeric-paren 0, table-words 11, var-words 1) =====121122J. Risk Financial Manag. 2022, 15, 1591239 of 13124Empirical Results125In order to conduct the Granger causality test between two time-series, we need to126ensure the data is stationary by making sure that none of our datasets has any unit root.127To test the stationarity of the variables, we use the Augmented Dickey–Fuller test and128the Kwiatkowski–Phillips–Schmidt–Shin test. The results are shown in Table 2. All the129variables are not stationary at I (0), whereas at I (1), all the time series variables show as130stationary. Therefore, we need to use first differenced data for all the time series variables131and follow the modified Granger causality processes to conduct the test.132Table 2. The result of the Unit root test.133Variable134Incl. Trend?135ADF Test136KPSS Test137Order of Integration138ln(TP1)139Without trend140−0.63191412.3861142With trend143−1.73421440.2268145dln(TP1)146Without trend147−17.50341480.0802149I (1)150With trend151−17.48581520.0688153I (1)154ln(TP2)155Without trend1560.41981572.3972158With trend159−2.81851600.1331161dln(TP2)162Without trend163−3.15301640.2189165I (1)166With trend167−3.21871680.1141169I (1)170ln(CV1)171Without trend172−0.80901732.3955174With trend175−1.48851760.1984177dln(CV1)178Without trend179−14.83981800.0976181I (1)182With trend183−14.83711840.0826185I (1)186ln(CV2)187Without trend188−0.39841892.3851190With trend191−3.72921920.2163193dln(CV2)194Without trend195−3.28611960.1088197I (1)198With trend199−3.30012000.0790201I (1)202Notes: The 5% critical values of the ADF and KPSS test statistics are, respectively, 2.87 and 0.463 (without trend)203and 3.42 and 0.146 (with trend). A significant test statistic indicates the rejection of the null hypothesis, which is204non-stationarity in the ADF tests and stationarity in the KPSS test.205Using the AIC test, we are able to determine the lag periods. The AIC test results for206the lag length used for the Granger causality test between the natural logarithm of the207first-hand TP and the natural logarithm of the first-hand CV are shown in Table 3. The208optimal lag length p is 2 (months) after first differencing dln(TP1) and dln(CV1), whereas209the maximum order of integration m is 1. Then, we use p + m, which is 3 (months), as210the lag length between dln(TP1) and dln(CV1). At the same time, the result between the211second-hand TP and the second-hand CV after first differencing dln(TP2) and dln(CV2)212shows that the optimal lag length p is 12 (months). By adding the maximum order of213integration (m) 1, the lag length we use for the Granger causality test between dln(TP2) and214dln(CV2) is 13 (months).215Table 3. Result of AIC tests.216Variable217Lag Length218AIC Result219dln(TP1)/dln(CV1)2202221−11.91756222dln(TP2)/dln(CV2)22312224−15.54021225Table 4 shows the results of Granger causality tests, which in the first-hand market,226the TP does not Granger-cause the CV, nor does the CV Granger-cause the TP while in227the second-hand market. In the secondary market, the null hypothesis of “dln(TP2) does228not Granger-cause dln(CV2)” is rejected at 5 × 10−5 the p-value, and the null hypothesis229of “dln(CV2) does not Granger cause dln(TP2)” is also rejected at 2 × 10−8 the p-value.230This indicates that the result shows that CV Granger-causes TP and vice versa in the231second-hand market.232