1# RECORD 129023b53e859d480f057e1aa633ff7548d6c7272# TITLE: Anchoring Effect in Chinas Real Estate Market3# YEAR: 2023.0 VENUE: Advances in Economics, Management and Political Sciences DOI: 10.54254/2754-1169/14/202308154# AUTHORS: Siyu Chen; Wenhao Wang; Lu-Hong Zhong5# PAGES: 8 CANDIDATE TABLE PAGES (1-based): [4]6# ABSTRACT: Since the reform and opening up, China's real estate industry has prospered rapidly and has become an important force driving rapid economic growth and promoting urbanization. In recent years, China's real estate industry has entered an inflection point of development, and real estate bubbles and cyclical sharp fluctuations in the real estate market have occurred from time to time. Behavioral finance has provided a new research direction for how to explain this fluctuation. Based on this, this essay uses the classic anchoring effect theory in behavioral finance theory to study the existence and characteristics of anchoring effect in China's real estate market from the micro level, and explains the entire mechanism through three applications. The first application empirically tested the existence of the anchoring effect in China's real estate market, and analyzed the difference between the anchoring effect in the eastern and western regions of China. It verified that the decision of housing prices in the central region was affected by the anchoring effect of the micro subject, while the anchoring effect in the eastern and western regions was not significant. The second application analyzed Hong Kong's secondary residential market, and a statistical model including anchoring effects was also presented in order to study the dynamics of house prices. The third application provides an improved Hedonic model, which combines various literature data to study the influence of age, growth area and other characteristics of home buyers on real estate prices under the anchoring effect.789===== PAGE 1 (score 5, numeric-paren 0, table-words 0, var-words 5) =====1011Anchoring Effect in China’s Real Estate Market 12Siyu Chen 1, a, †, *, Wenhao Wang2, † and Luhong Zhong3, † 131 School of Economics, Fujian Normal University, Fuzhou 350108, China 142 Suzhou Foreign Language School, Suzhou 215000, China 153 Ulink College of Shanghai, Shanghai 201600, China 16a. 104022020006@student.fjnu.edu.cn 17*corresponding author 18†These authors contributed equally to this work. 19Abstract: Since the reform and opening up, China's real estate industry has prospered rapidly 20and has become an important force driving rapid economic growth and promoting 21urbanization. In recent years, China's real estate industry has entered an inflection point of 22development, and real estate bubbles and cyclical sharp fluctuations in the real estate market 23have occurred from time to time. Behavioral finance has provided a new research direction 24for how to explain this fluctuation. Based on this, this essay uses the classic anchoring effect 25theory in behavioral finance theory to study the existence and characteristics of anchoring 26effect in China's real estate market from the micro level, and explains the entire mechanism 27through three applications. The first application empirically tested the existence of the 28anchoring effect in China's real estate market, and analyzed the difference between the 29anchoring effect in the eastern and western regions of China. It verified that the decision of 30housing prices in the central region was affected by the anchoring effect of the micro subject, 31while the anchoring effect in the eastern and western regions was not significant. The second 32application analyzed Hong Kong's secondary residential market, and a statistical model 33including anchoring effects was also presented in order to study the dynamics of house prices. 34The third application provides an improved Hedonic model, which combines various 35literature data to study the influence of age, growth area and other characteristics of home 36buyers on real estate prices under the anchoring effect. 37Keywords: anchoring effect, real estate market, house price 381. 39Introduction 40One of the great lessons of the 2008 international financial crisis is the harmfulness of house price 41bubbles to the macro economy. As an important asset for enterprises and residents, real estate 42possesses the dual characteristics including consumer goods as well as investment goods, and its price 43fluctuations may have a greater impact on the real economy and financial system in depth and breadth 44than stocks and other financial assets. 45Since the policy of reform and opening-up introduced, China's real estate industry has undertaken 46tremendous changes. However, its development in China has not been smooth sailing, and some 47serious problems have been exposed beneath the surface of the property market boom. From a 48nationwide perspective, the development of China's real estate market has shown an overheated trend, 49Proceedings of the 2nd International Conference on Business and Policy Studies50DOI: 10.54254/2754-1169/14/2023081551© 2023 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.052(https://creativecommons.org/licenses/by/4.0/).53174545556===== PAGE 4 (score 66, numeric-paren 45, table-words 6, var-words 2) =====5758the change of the real estate market transaction volume preceded real estate price, and the final VAR 59estimation result was used to test the lagged positive correlation between real estate market 60transaction volume and real estate price to test whether the anchoring effect exists. 61Table 1: Descriptive statistics of the data. 62 63Variate Sample 64AVG 65Variance 66MIN 67MAX 68the 69National 70P 712652 724374.511 733410.051 74650.3289 7522209.08 76V 772652 7824159956 792496963 805100 8123800000 82East 83P 841404 855769.426 864043.801 871076.983 8822209.08 89V 901404 912729427 922733150 935100 9423800000 95Mid 96P 97624 982711.298 991332.947 100650.3289 1016195.997 102V 103624 1042168816 1052204528 10647600 10716300000 108West 109P 110624 1112899.171 1121242.315 113741.91 1146417.433 115V 116624 1171957955 1182086896 1197450 12013100000 121Table 2: Results of panel unit root tests on economic variables. 122 123Testing 124Method 125LLC 126Breitung 127IPS 128ADF-Fisher 129PP-Fisher 130the 131National 132lnp 133-6.28901 134-4.85440*** 135-16.7042*** 136-365.591*** 137694.343*** 138Inv 139-13.4258*** -2.72818*** 140-13.5794… 141-304.871*** 142917.908*** 143East 144lnp 145-4.09745*** -5.46881*** 146-14.678*** 147238.82*** 148332.064… 149Inv 150-6.65464*** -1.70474*** 151-6.86672*** 152106.020*** 153446.246*** 154Mid 155lnp 156-2.79502*** -2.45001*** 157-9.46389*** 158101.508*** 159182.929*** 160Inv 161-19.0694*** 162-1.77311** 163-14.3103*** 164171.524*** 165249.731*** 166West 167lnp 168398539*** 169-1.78435*** 170-3.01980*** 17125.2604*** 172179.350*** 173Inv 1741.45726 175-1.28279* 176-3.43872*** 17727.3272*** 178221.931*** 179*, **, *** indicate stationary at the 10%, 5% and 1% significance levels respectively 180Table 3: Estimated results of panel VAR. 181 182Inp 183 184the National 185East 186Mid 187West 188Lnv(-l) 1890.301227*** 190-0.0702564** 1910.09416*** 1920.0082298 193Lnv(-2) 194---- 195-0.0522693 1960.0312033 1970.0320357 198Lnp(-l) 1990.627542*** 2000.57088*** 2010.403938*** 2020.5080872*** 203Lnp(-2) 204---- 2050.5300592*** 0.3984971*** 2060.4186571*** 207 208By analyzing the data and tables above [12], experimenters could draw the following conclusions. 209Table 1 indicates that the average real estate price in the central and western region were less than 210that in the eastern regions. The fluctuation range was a step-like decrease in the eastern, central and 211western regions, and from the perspective of the average transaction volume of real estate, the eastern 212region has the largest transaction volume, followed by the central region, and the western region has 213the least average transaction volume. Table 2 suggests that the nationwide variables lnp and lnv were 214both stationary at the 1% significance level and had no unit root. While the panel data of the two 215variables (except for individual cases) in the east, middle and west regions did not have a unit root, 216and they were all stationary time series. Table 3 verifies that from a national perspective, the current 217Proceedings of the 2nd International Conference on Business and Policy Studies218DOI: 10.54254/2754-1169/14/20230815219177220221222===== PAGE 5 (score 3, numeric-paren 0, table-words 0, var-words 3) =====223224housing price was affected by the transaction volume of the previous issue, and there was a positive 225correlation between them. That is, in China's commercial housing market, there was a lagging positive 226relationship between transaction volume and housing price. At the same time, the experimenters also 227found that the price has a strong autocorrelation, and investors would adjust the current price 228according to the historical price of the real estate. The rise in the past price would cause the current 229price to rise, which was caused by the psychological expectations of consumers. In other words, 230consumers set their own anchors through such expectations. As for the eastern, central, and western 231regions, the anchoring effect was dissimilar in different regions. The determination of housing prices 232in the central region was affected by the anchoring effect of the micro-subject, while it was not 233significant in the western and eastern regions. 234In summary, the anchoring behavior among the main part of consumers and the anchoring effect 235in the market are the manifestation of the imperfect operating mechanism and development of the 236real estate market. Targeted recommendations are put forward for investors, real estate developers 237and intermediaries, and the government. For investors, strengthening the skills of collecting and 238identifying information and the ability to control cognitive errors and psychological disorders are 239important. For real estate developers and intermediaries, though strengthening the construction of the 240credit system of real estate enterprises is tough to some extent, it is worth trying. The government 241should establish a transparent real estate market transaction information platform and evaluation 242mechanism. Furthermore, it is responsible to formulate differentiated policies for the development of 243real estate markets in different regions. 2442.2. Application in the Hong Kong Second-Hand Housing Market 245An appealing characteristic of Hong Kong’s secondary housing marketplace is its extreme 246competition [10]. There are a whole lot of customers and dealers with inside the marketplace [13]. 247Estate sellers' preferred fee is simply 2 according to cent of the acquisition fee (1 according to cent 248for every client and seller). The aggressive nature of the marketplace makes strategic conduct via way 249of means of customers, dealers or sellers much less likely, presenting best situations for checking out 250each cognitive bias. 251The assumption is that customers peg the cost of the house to its preceding buy fee. As a 252prerequisite for figuring out the anchoring impact, the client needs to recognize the preceding buy 253fee. 254In fact, the wants for these records are enormous. Between 2001 and 2010, the common quantity 255of searches according to year turned into 4.5 million (out of a populace of approximately 7 million 256primarily based totally on 2011 figures), at the same time as the quantity of second-hand transactions 257according to yr turned into much less than 200,000 [14]. This indicates that customers do take these 258records into consideration whilst creating a buy decision. The Internet has made it less expensive to 259get these records. The overall quantity of land sign in searches is at the rise. While the quantity of on-260line searches greater than doubled among 2001 and 2010, the quantity of counter searches declined 261step by step over the equal period. A direct implication of this fashion is that anchoring results have 262turn out to be more and more crucial over the years. 263Using housing transaction statistics from the Economic Property Research Center (EPRC) as the 264number one statistics source. 265Utilizing a pattern of repeat sales, researchers discover each anchoring and loss aversion with 266inside the Hong Kong secondary housing marketplace. then gifting an easy version to expose the 267effect of those cognitive biases on residence fee dynamics. In the version, because of those cognitive 268biases, each fee dispersion and quantity are definitely associated with common residence prices. In 269addition, taking into consideration loss aversion, a smaller anchoring impact reduces fee dispersion 270Proceedings of the 2nd International Conference on Business and Policy Studies271DOI: 10.54254/2754-1169/14/20230815272178273