Phân tích mối quan hệ giữa chiến lược rao giá, giá bán, thời gian rao bán và khả năng bán nghiên cứu thị trường nhà ở riêng lẻ TP HCM TT

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY NGUYEN THI BICH HONG THE RELATIONSHIPS BETWEEN LISTING PRICE STRATEGY AND SELLING PRICE, TIME ON MARKET AND PROBABILITY OF SALE THE CASE OF SINGLE – FAMILY HOUSING MARKET IN HO CHI MINH CITY Major: Economics Development Code: 9310105 SUMMARY OF THESIS Ho Chi Minh City – 2021 The research was conducted and completed at University of Economics Ho Chi Minh city: Tutors: PhD Pham Khanh Nam PhD Nguyen Hoang Bao Academic advisors: Reviewer 1: ………………………………………………… Reviewer 2: ………………………………………………… Reviewer 3: ………………………………………………… The dissertation will be defended at University of Economics Ho Chi Minh City At hour day month year The thesis can be found at the following library: ……………………………………………………………… ……………………………………………………………… 1.1 Research background Every seller wants as higher selling price and as shorter time on market as possible However, according to the theoretical framework of Simon (1995) and later theories of Wheaton (1990), Yavas (1992), Krainer & LeRoy (2002), Anglin & et al (2003), Lin & Vandell (2007), Cheng & et al (2008), these are two conflicting objectives That means home sellers always have to trade between these two objectives Accordingly, as home sellers spend more time searching for potential buyers, they will have more likelihood of finding more quality buyers who are willing to pay a higher price for the for-sale property In this relationship, the selling price acts as a tool to determine the trade-off of the seller, a high listing price is a seller's signal of a high selling threshold and thus limiting the number of buyers This leads to a longer listing period, but the selling price will be higher due to better quality of potential buyers (Hoeberichts et al., 2008) However, in particular, 8/18 studies compiled by Sirmans et al (2005) found a relationship contrary to the above theory between selling price and for-sale time Similarly, Johnson et al (2008) synthesized 108 relevant studies during the period of 1995-2007, only 29 studies had the right results in theoretical relationships, 52 studies found the inverse relationship, and 24 cases found no relationship between these two factors Benefield et al (2014) summarized 197 related studies and found similar findings with 100 studies identifying the theoretical inverse relationship In order to explain the inverse correlation between selling price and time on market explored by emprirical studies, basing on retail model of Lazear (1986), Taylor (1999) has developed a theoretical framework of market discrimination Accordingly, when the quality of housing is difficult to be observed by buyers, a house with a long for-sale period will be a signal of having "bad" quality and therefore the transaction price of the house will decrease The relationship between the price and time on market is both theoretically and empirically ambigious, so the role of the listing prices in transaction prices and time on market has not yet been agreed among researchers Therefore, many researchers suggest that more empirical studies should be conducted on the impact of factors on the relationship between the selling price and time on marrket, and the probability of selling in different real estate markets, especially newly developed and small-sized housing markets, to contribute to raising awareness and improving the efficiency of these markets (McGreal, Adair & Brown, 2009; Filippova & Fu, 2011; Cirman et al., 2015) In particular, there is only a small number of research topics on the sale ability of house around the world, such as measure the effect of selling strategy (Kluger & Miller, 1990; Hui et al., 2012); atypical degree of the house (Krainer, 1999); the value of the house (Smith, 2010); sellers’ motivation (Johnson et al., 2008; Cirman et al., 2015) on probability of sell of a house Although having found to have an impact on the probability of sale of the house, it is limited to measuring the static impacts of these factors However, the longer the time on market, the more likely homebuyers’ behavious will change (Taylor, 1999) so according to the author, the impact of factors on the probability of sale will varies accordingly However, this issue has not yet mentioned on prior studies This is the research gap need to be considered In addition, the housing studies in Vietnam mostly focus on the impact of factors on house prices (Kim, 2004, 2007; Bui, 2020a, 2020b; Seo and Kwon, 2017) The research in buyer-seller behavior strategy, the liquidity and the selling ability of the house are not available in Vietnam Therefore, within this thesis, regarding home sellers, the author conducts research on the sellers' behavior, in particular, the role of listing price strategy in selling price, time on market, and the probability of sale in the detached housing market in Ho Chi Minh City In particular, the impact of the listing price strategy on the probability of sale of a house will be measured against the different time on market (1 month, months, months, and months) to determine the fluctuations of this effect over time on market In homebuyers site, a finding of the author in understanding the theoretical frameworks about current homebuyers' behavior is that there is no theoretical framework that analyzes the impact of their current houses’ characteristics (houses where they are residing in) on their current housing search behavior In particular, a buyer with experience in some charecteristics of the prior house such as the the heat, the stuffiness of the house suffered by the sunshine in the afternoon will concern more about the view of the new house However, there are no researches mentioned about these issues both in empirical and theory Therefore, in this thesis, the author also conducts a theoretical framework to analyze the impact of homebuyers’ prior houses on their current search behavior 1.2 Research Objectives With the aforementioned subjects, the author conducts research on two aspects of the housing market in HCM city: analysis of housing supply and analysis of housing demand On the supply side, the author analyzes the seller's bidding strategy On the demand side, this thesis focuses on house buying behavior under the influence of current house’s characteristics Specifically: - Analyze the relationship between the listing price strategy of the seller and the selling price, the time on market, and the probability of sale corresponding to different periods - Use the search behavior theory to develop a theoretical model that analyzes the impact of characteristics of the buyer's current home on their buying behavior and tests this correlation by empirical results 1.3 Research methods Two different research methods will be applied to the two research objectives With the aim of analyzing the relationship between the listing price strategy and the selling price, the time on market, and the probability of sale corresponding to different periods The author will apply the 3-step research method In particular, step is be to determine the listing price strategy based on the difference between the actual selling price and the market price of the house, which is estimated from the properties of the house using hedonic model Step measures the effect of this listing price strategy on the transaction price and time on market through quantitative models Step measures the impact of the placement strategy on the probability of sale through the Cox Proportional Hazard Model with 1, 3, 6, and 9-month sales timeline, which is to consider changes in the influence of these factors over these sales time points With the objective of developing a theoretical framework analyzing the influence of old houses’ properties on the current housing search behavior of homebuyers, the author will outline the theory related to search behavious of current home buyers, then upholding and developing their theoretical framework The conclusions of the theoretical framework will be tested experimentally to determine the validity of the developed theoretical framework 1.4 Thesis Structure Follow this chapter are chapter 2: Theoretical basis; Chapter 3: Results of measuring the impact of pricing strategies; Chapter 4: Develop a theoretical framework analyzing the influence of homebuyers’ current dwellings on their behavior; Chapter 5: Conclusions and recommendations Chapter 2: Theoretical basis 2.1 Some related concepts In this content, the author presents a number of related concepts: such as detached houses, prices related to housing, listing price strategies including under-pricing and over-pricing strategies 2.2 Theoreical basis In this content, the author presents the following groups of theoretical bases: 2.2.1 The group of theories related to the relationship between listing prices, selling prices and time on market on the housing market This consists of theoretical basis on the relationship between price and time on market by Cheng et al (2008), framework theory of psychological stigma of Taylor (1999), theoretical model of fishing behavior by Sun and Seiler (2013) Including: * The theoretical basis for the relationship between housing price and time on market by Cheng et al (2008) was developed based on the theories of Wheaton (1990), Krainer & LeRoy (2002), Anglin & đồng (2003), Lin&Vandell (2007) On the basis of the theory by Cheng et al (2008), a home seller would wait for n home buyers to come and offer a price, and then the buyer would negotiate with the buyer who offers the highest price to sell the house And then it is the problem of the seller to dread the risk of long time waiting, that is, to determine the optimal n*, to achieve the highest level of risk-adjusted selling price expectations, in an ideal case when the buyer does not leave the market, and even if there is a percentage of the buyer leaving the market As a result of the theoretical framework, Cheng et al (2008) identified a positive relationship between long sales and high expected risk-adjusted trading prices in both cases * According to the theoretical framework of Taylor's discriminatory psychology (1999), the process of selling a house consists of phases, buyers in each phase will bid together, the winning bidder will review the house and decide based on their judgement, knowing that the judgement always entails a rate of error Theoretical results show that, when buyers in phase have not been reviewed in phase 1, probability of ending up with good houses in phrase is always lower than in phrase Therefore, according to Taylor (1999), longer listing time is a signal of the bad quality of the house (called stigma signal), so the relationship between transaction price and time on market is negative * The theory of the fishing behavior of sellers by Sun & Seiler (2013) is also a process of selling a house in phases with limited time for sale, so in the final stage, the seller has to sell at the average price market, and therefore in phase the suggested price x must be greater than the market average to be considered Since then Sun & Seiler (2013) determines the seller's maximum acceptable x* price and determines the fishing behavior is the setting of the above acceptable minimum price compared to the market average price of the house The theoretical results of Sun & Seiler show that in most cases, sellers always have a motive for fishing, and the higher the quality of the house (the greater the value to the seller), the greater the fishing behavior will be showed by sellers From these three theories, the author of the thesis proposes two research hypotheses that need to be clarified: 10 H0: An over-listing price strategy will signal a high selling threshold and thus increase the selling price of a house but at the same time prolong the time on market, derived from the theory of Cheng et al (2008) H1: An under-listing price stategy will be a signal to buyers about the possibility of a problem in the house (derived from the theory of Sun & Seiler, 2013), and therefore the house will become difficult to sell at lower prices and with longer time on market (derived from Taylor's theory, 1999) 2.2.3 Theory of the Cox home sale ability model To measure the impact on the likelihood of a risk (probability of sale), in the past, researchers used the Survival model to measure the extent of the impact of false factors that were deviated from the baseline rule of the study subjects, but the problem is that this basic rule of life is not observable, which limits the applicability of the survival time model However, Cox (1972) has developed into a Cox risk model with the advantage of not needing to know the basic rules of risk due to the comparison with the standard subjects of the sample instead of the rules Basically, this has expanded the applicability of the Cox risk model (Cajias & Freudenreich, 2018) Therefore, the dissertation will also apply the Cox model to measure the impact on probability of sale of a house (should be called the Cox sale model), and establish the Cox model with timelines of 1, 3, 6, months to analyze the fluctuations of these effects over time Cox model will estimate the HR ratio (hazard ratio) of the explanatory variables in the model and thus tell us whether that variable has an effect of increasing (HR> 1) or 14 Then, the dependent variables PS and TOM and the explanatory variables Sj, Lj, Nj of the house, used in equations (1), (2), (3), (4), are discussed in section 3.1.1.1 and 3.1.1.2 3.1.2 Research data The author surveyed 460 individual housing transactions in the urban area of Ho Chi Minh City based on the probability of stratification by geographic area, the districts with active transactions will be allocated a large number of samples and vice versa Subjects of the survey are separate housing transactions (excluding villas) in the secondary market with the participation of brokers to ensure (1) the negotiated position between buyers and sellers is equivalent (secondary market), (2) sellers obtain information about average prices of similar houses in the area (broker) when making decisions and listing prices, (3) the similarity in type of house and land use purpose (individual houses in urban areas and excluding villas) to reduce the variance change In particular, data on prices, time on market, structural and location features were asked by brokers, while accessibility and surroundings were asked by homebuyers (also information about their old houses) Table 3.2: Descriptive statistics of data Varia name Price Tom Age Slotarea Floorarea Outside Shape Wide Long Unit Million VND Days Years square meter square meter – oldest; – newest square/grew bigger otherwise meter meter Description Price Time on market Age of house Lot size Square footage Outside charecteristics Mean 932.45 114.38 8.93 71.21 186.75 2.531 Std Dev Min Max 777.35 900 26 600 130.14 884 7.18 30 36.64 25 320 111.70 44 600 1.293 shape of the land 0.82 0.39 Width of house Long of house 4.49 15.66 1.44 4.93 6.8 12 32 15 Nbedr Nbathr Sun Face Dstreet Widestreet Dcbd Tcbd Tworkpla Safe Waste Smelly Noisy Flooding No of rooms No of rooms sunshine otherwise alley/ frontage meter Number of bedrooms Number of bathrooms house facing afternoon sunshine (hot) Road frontage Distance from frontage width of the road in meter front of the house Kilometer Distance to CBD Minutes Time to CBD Minutes Time to working place is worst Status of safety in is best neighbourhood no collection system Status of garbage otherwise collection system is worst Status of smell in is best neighbourhood is worst Status of quiet condition is best in neighbourhood no flooding Status of flooding in flooding neighbourhood 3.83 3.50 1.88 2.00 1 14 15 0.36 0.48 0.23 89.18 0.42 163.39 0 1 000 8.69 6.30 30 8.15 22.98 14.38 3.92 10.22 8.00 0.6 1 16.4 60 40 5.83 1.21 0.61 0.37 4.68 1.95 5.06 1.81 0.90 0.29 (Source: Research calculations from self-survey data) 3.2 Identifying pricing strategies 3.2.1 Research methodology to identify pricing strategies This is the first step in the 3-step method of the thesis to measure the impact of the listing price strategy with the objective of indentifying the DOP listing price strategy for the house The use of a direct difference between the listing price and the actual trading price will cause potential deviations due to the concurrency (stick) between these two variables (Yavas & Yang, 1995; Hui & co., 2012), so the author applied the method proposed by Kluger & Miller (1990) and successfully applied by related studies, which is an estimate of the expected market price of the house through the hedonic model and use this price range to calculate the DOP Therefore, in this step, the author will establish a hedonic model in the form of a model (1) and use it to estimate the market's expected market price, and then use this 16 price to calculate the DOP deviation compared to the actual listing price 3.2.2 The results of the seller's listing price strategy model Table 3.3: Estimation results of house price models Model Variables Lnage lnfloorarea floorareasqu lnslotarea slotareasqu Face Shape widestreet Acar dstreet lntworkpla Lntcbd Sun Safe Waste Smelly Noisy flooding slig_flood stri_flood _cons D C.Dummy R-squared Prob(F) Root MSE Dep Var N of obs Model Robust VIF Std Err Coef -0.0318 0.1818*** 1.26E-07 0.6044*** -3.5E-06* 0.1056* -0.0892** 0.0153*** 4.8235*** 0.0202 0.0514 3.7E-07 0.0741 2.1E-06 0.0631 0.0348 0.0041 0.3116 Yes 3.4 3.7 5.1 2.9 2.8 1.2 2.6 Model Robust VIF Std Err Coef -0.0417** 0.163*** Model Robust VIF Std Err Coef Robust VIF Std Err Coef 0.0201 0.0391 3.5 2.3 -0.0374** 0.1522*** 0.0178 0.0374 3.6 2.3 -0.0419** 0.1551*** 0.0179 0.0373 3.7 2.3 0.6253*** 0.0715 -3.9E-06* 2.2E-06 0.0805 0.0584 -0.0843** 0.0334 0.0154*** 0.0037 0.1821** 0.0919 -1.5E-04* 8.5E-05 -0.0509** 0.021 -0.0694*** 0.0247 0.0672*** 0.0242 5.1 2.9 3.3 1.2 2.8 1.3 1.8 1.2 1.8 1.2 0.5297*** 0.0556 2.8 0.5261*** 0.0559 2.8 -0.0894* 0.0346 0.018*** 0.0021 0.1466* 0.0834 -1.5E-04* 7.9E-05 -0.0471** 0.0213 -0.067** 0.0296 0.0543** 0.024 0.0143 0.0137 0.0585 0.0447 0.0362*** 0.0094 -0.0301*** 0.0105 -0.0904** 0.0425 1.2 1.5 1.3 1.5 1.3 1.9 1.3 1.7 1.2 1.9 1.3 -0.0855** 0.0181*** 0.1456* -1.5E-04* -0.0488** -0.0638** 0.0565** 0.0146 0.0568 0.0363*** -0.0294*** 0.0348 0.002 0.0818 8E-05 0.0212 0.0298 0.0241 0.0138 0.0449 0.0094 0.0105 1.2 1.5 1.3 1.6 1.3 1.9 1.3 1.7 1.2 2 -0.0878** 0.0428 -0.1957*** 0.063 5.4636*** 0.2621 Yes 1.7 1.7 5.0776*** 0.8706 0.24958 lnprice 448 0.2929 Yes 0.8795 0.24195 lnprice 448 5.4684*** 0.2605 Yes 0.888 0.23412 lnprice 448 0.8887 0.23364 Lnprice 448 Note: - The models in the table are estimated by the method of OLS with strong standard errors - *, **, *** respectively represent significance levels at 10%, 5% and 1% respectively - Checking VIF variables in the model without multicollinearity signs (Source: Estimates based on survey data of the study) Excluding a number of variables with multicollinearity, model results will be used to estimate the expected market price based on the properties of the house because of the higher degree of interpretation and accuracy The difference between the actual listing price and the expected market price will then represent the sellers' pricing strategy 3.3 Measuring the effect of the listing price strategy on the price and time on market 17 3.3.1 The research method applied in this step This is the second step in the 3-step method, the goal of this step is to measure the impact of the listing price strategy on house prices and time on market First, the author applies model in table 3.3 to estimate the expected market price of housing (variable Pricef) And the DOPj house listing price strategy will be determined as follows: The DOPj house listing price strategy will then be used as an explanatory variable in equations (2 ') and (3) to measure the impact of the listing price strategy on transaction prices and time on market of the house to answer two hypotheses H and H1 of the thesis Particularly for equation (2 ') due to limiting the potential proximity between DOPj and Pis, the author will replace DOPj with the dummy variable Dum_DOPj 3.3.2 Results of measuring the impact of a listing price strategy on the selling price As mention above, the listing price strategy (DOP) in this case will be change to dummy variable Dum_DOP Table 3.4: Results of measuring the impact of listing price strategies on housing prices Model Variables Model Model Coef Robust VIF Std Err Coef Robust VIF Std Err Coef Dum_DOP 0.3053*** 0.0218 1.06 0.3187*** 0.0206 1.08 0.3513*** 0.0189 lnage lnfloorarea floorareasqu lnslotarea slotareasqu face shape widestreet acar dstreet lntworkpla lntcbd sun safe waste smelly noisy -0.0505*** 0.1696*** -5.35E-08 0.6055*** -2.11E-06 0.1034* -0.1082*** 0.0167*** 0.0176 0.0383 2.3E-07 0.0556 1.3E-06 0.0533 0.0267 0.0032 -0.0651*** 0.0167 0.1287*** 0.0276 3.5 2.3 -0.0618*** 0.1037*** 0.6226*** -2.5E-06** 0.0598 -0.1008*** 0.0183*** 0.1301* -1.9E-04*** -0.0692*** -0.0987*** 0.0733*** 5.1 2.9 3.4 1.2 2.8 1.3 1.8 1.3 1.8 1.2 3.4 4.0 3.7 5.1 2.9 2.8 1.2 2.6 0.0506 1.2E-06 0.0455 0.0241 0.0026 0.0667 5.9E-05 0.0165 0.0169 0.0178 Model Robust VIF Std Err Coef Robust Std Err VIF 1.1 0.3553*** 0.0186 1.1 0.0143 0.0258 3.6 2.3 -0.0692*** 0.0143 0.1078*** 0.0255 3.7 2.3 0.5514*** 0.0348 2.8 0.5458*** 0.0349 2.8 -0.1012*** 0.02*** 0.0934 -1.7E-04*** -0.0645*** -0.1003*** 0.0577*** 0.0299*** 0.0788*** 0.0467*** -0.0385*** 0.0218 0.0013 0.0595 4.7E-05 0.0153 0.0179 0.0159 0.0094 0.03 0.0067 0.0072 1.2 -0.0951*** 0.0221 1.5 0.0201*** 0.0012 1.3 0.0911* 0.054 1.5 -1.6E-04*** 4.7E-05 1.3 -0.0675*** 0.015 1.9 -0.0954*** 0.0178 1.3 0.0612*** 0.0156 1.7 0.0307*** 0.0095 1.2 0.0763** 0.0301 0.0469*** 0.0067 1.9 -0.0375*** 0.0071 1.2 1.5 1.3 1.6 1.3 1.9 1.3 1.7 1.2 1.9 18 flooding slig_flood stri_flood cons D.C.Dummy 4.7527*** R-squared Prob(F) Root MSE Dep V N of obs 0.2352 Yes 5.3157*** 0.917 0.2001 Lnprice 448 0.2003 Yes -0.1449*** 0.0234 5.6097*** 0.1648 Yes 0.9294 0.18546 lnprice 448 1.3 -0.1414*** 0.0236 -0.3124*** 0.0391 5.6037*** 0.1668 Yes 0.9471 0.16104 lnprice 448 0.949 0.15844 lnprice 448 Notes: - The models in the table are estimated by the method of OLS with strong standard errors - *, **, *** respectively represent significance levels at 10%, 5% and 1% respectively - Checking VIF variables in the model without multicollinearity signs (Source : Estimates based on survey data of the study) Estimated results show that the over-listing price strategy has a significant impact at 1% with the level of helping increase 35% of the transaction price of the house compared to the strategy of under-listing In addition to the listing price trategy, a number of other properties of a home have been identified as having an influence on the selling price of a property such as age, area, type of land, proximity to the center, workplace, accessibility and safe and hygienic surroundings 3.3.3 Results of measuring the impact of listing price strategies on time on market Table 3.5: Results of measuring the impact of the listing price strategy on time on market Model Variables DOP Lnage Lnfloorarea floorareasqu Lnslotarea Slotareasqu Face Shape Widestreet Acar Dstreet Lntworkpla Lntcbd Sun Safe Waste Smelly Noisy Flooding slig_flood stri_flood _cons Coef Model 10 Robust Std VIF Err -0.4711* -0.1009 -0.5771** 2.57E-06* 0.6558*** 0.2856 0.1149 0.2295 1.4E-06 0.2234 1.05 3.39 3.97 3.71 2.76 0.2386 0.075 -0.0114 0.2257 0.1746 0.015 2.78 1.19 2.56 4.2946*** 1.4643 Coef Model 11 Robust Std VIF Err Coef Robust Std Err VIF -0.4849* -0.0731 -0.2536 0.2817 0.1171 0.1572 1.05 3.5 2.24 -0.449* -0.1184 -0.1785 0.2646 0.1096 0.1485 1.04 3.58 2.31 0.6703*** 0.2231 2.7 0.8452*** 0.2312 2.83 0.5079** 0.0322 -0.025 -0.0811 9.91E-04** 0.0693 0.4075*** -0.0727 0.244 0.1672 0.0158 0.256 0.00039 0.1048 0.1369 0.1423 3.34 1.22 2.81 1.28 1.77 1.24 1.84 1.24 0.0319 0.0028 -0.2894 5.75E-04 0.0123 0.3694*** -0.1137 -0.1885*** -0.1717 -0.1144*** 0.1019** -0.2914 0.1693 0.0117 0.2882 0.00038 0.1063 0.1411 0.1446 0.0576 0.1537 0.0416 0.0462 0.233 1.24 1.49 1.28 1.54 1.27 1.88 1.27 1.65 1.17 1.98 1.94 1.27 1.0985 1.4167 1.9464 1.4572 1.7 1.7 19 D.C.Dummy R-squared Prob(F) Root MSE Dep Var N of obs Yes Yes Yes 0.2601 1.2277 Lntom 448 0.2803 1.2165 Lntom 448 0.3133 1.194 lntom 448 Notes: - The models in the table are estimated by the method of OLS with strong standard errors - *, **, *** respectively represent significance levels at 10%, 5% and 1% respectively - Checking VIF variables in the model without multicollinearity signs (Source: Estimates based on survey data of the study) The significance level of time on market models that fluctuates between 26% -31% is consistent with other authors' time-forsale models The house's listing price strategy (DOP) was found to have a 10% meaningful impact which shortened the time on market of the home Thus, along with Model 8, the author concludes that we need to reject the hypothesis H and accept H1 Accordingly, a high bidding strategy acts as a signal of the "good" quality of the house, and therefore the buyer not only accepts to pay a higher price, but also has an incentive to buy faster The reason is due to the shortage of housing in the market in the research period, with the absorption rate increasing up to 50% 59% In addition to the listing price strategy, small houses, near the center, in a secure area and good environment are also factors that attract buyers and help shorten the time on market of the house Hình 3.3 The house price index in HCM 20 Nguồn: Savills Research and Consultancy 3.4 Measure the impact of the listing price strategy on probability of sale of a house 3.4.1 Research method applied in this step Cox saleability model is developed from the viability model, so there are two main components: the ability to survive S(t) and the likelihood of risks h(t) Where S(t) is the likelihood that a house still exists in the market at time (t) and h(t) is the likelihood of a risk occurring at time t So, the Cox model requires some changing observations (censored observations) and others not change the status (censoring observations) between before and after time t Therefore, to solve this problem, the author breaks the study time into several timelines: 1, 3, 6, months (time points t) And for each timeline, the censorimg variable will get a value of for homes that have a shorter listing time, and vice versa This means we have moderating variables corresponding to timelines: onemonth, threemonths, sixmonths, ninemonths The Cox models measure the effect of the listing price strategy and other properties on the probability of sale with the dependent variable in the models being the time on marekt under the same censorship conditions 3.4.2 The results measuring the impact of listing price strategy on the probability of sale Table 3.7: Estimation results of saleability of Cox model For for-sale times of month, months, months, months - Breslow method for ties 1-month-Cox model Haz Ratio DOP Age Lnfloorarea 2.0827* 1.0106 1.207 3-month-Cox model Robust Std Haz Ratio Err 0.7966 0.0176 0.3023 1.645* 1.0107 1.0723 6-month-Cox model 9-month-Cox model Robust Std Err Haz Ratio Robust Std Err Haz Ratio Robust Std Err 0.4264 0.0111 0.1700 1.5549* 1.0243 1.2188 0.3829 0.0096 0.1755 1.4224 1.0258 1.1882 0.3293 0.0095 0.1632 21 lnslotarea widestreet Acar Dstreet Lntcbd lntworkpla Sun Safe Waste Smelly Noisy Flooding 0.3305*** 0.9829 3.406* 1.0007 0.9832 0.9238 1.1939 1.1533* 1.6523*** 1.2283*** 0.829*** 1.1224 0.1228 0.0212 2.4489 0.0005 0.2434 0.1797 0.2527 0.0906 0.2781 0.0974 0.0500 0.4305 No of subjects = 448 No of failures = 108 Time at risk = 51242 Wald chi2(15) = 60.32 Prob > chi2 = Log pseudollh = -626.55 0.622** 1.0023 0.8632 1.0005 1.1744 1.0007 1.1703 1.1541* 1.7565*** 1.153*** 0.8402*** 0.9677 0.1439 0.0119 0.2476 0.0003 0.1904 0.1177 0.1586 0.0641 0.2191 0.0523 0.0328 0.2175 No of subjects = 448 No of failures = 260 Time at risk = 51242 Wald chi2(15) = 91.65 Prob > chi2 = Log pseudollh = -1469.78 0.5898** 0.9909 0.8772 1.2398 1.1726 1.107 1.1472*** 1.6931*** 1.0811** 0.8762*** 1.0754 0.1240 0.0110 0.2255 0.0004 0.1737 0.1165 0.1333 0.0603 0.2306 0.0403 0.0322 0.2192 No of subjects = 448 No of failures = 366 Time at risk = 51242 Wald chi2(15) = 62.43 Prob > chi2 = Log pseudollh = -1993.93 0.5856** 0.9945 0.9281 1.1121 1.1699 1.1336 1.1359** 1.6146*** 1.0802** 0.892*** 1.0569 0.1236 0.0097 0.2506 0.0004 0.1366 0.1139 0.1333 0.0603 0.2317 0.0398 0.0332 0.2203 No of subjects = 448 No of failures = 380 Time at risk = 51242 Wald chi2(15) = 56.09 Prob > chi2 = Log pseudollh = -2056.96 Notes: - The models in the table are estimated by the method of OLS with strong standard errors - *, **, *** respectively represent significance levels at 10%, 5% and 1% respectively - Checking VIF variables in the model without multicollinearity signs (Source: Estimates based on survey data of the study) The results show that the listing price strategy has a strong impact on the probability of sale of houses in the first 30 days of sale, after that, this effect dimimishes and after 180 days of sale, there is no effect This shows that the over-listing price strategy is a "good" signal of the quality of the house to the buyer, which increases the probability of sale of the house, but when the time for sale is prolonged, it is a good signal “not good” effect on house quality (Taylor, 1999) and thus counteract the impact of the listing price strategy The same problem was found for the house area factor A small single-family house always attracts buyers (due to budgetary issues) so it is likely to sell out in the first 30 days of sale, but then the time for resale is a bad signal which wore out impact of this factor The ability of cars entering the house is different, according to the author, is due to the difference between the two groups of 22 buyers, the buyer has a financial surplus and those with financial constraints Chapter 4: Developing a theoretical framework that analyzes the impact of homebuyers’ current dwellings on their behavior 4.1 Developing theoretical framework Each house listing in the market conclude a vector of charecteristics, Xi, then give the buyer the utility u(x i) and it has the value for buyer is with the price Pi If buying, the buyer gains the benefit , knowing Gi ∈ [- ∞, + ∞] and follow the distribution rule F(G) (Cronin, 1982; Turnbull&Sirmans,1993; Tu & đồng sự, 2016; Qiu&Zhao, 2018) If you not buy and continue searching, the buyer can still benefit from the old house with the vector of charecteristics x0 and gains the benefit from the old house during the search, the costs come from finding SC and expecting the benefit of the house to find E (G), i.e The benefit of continued search is + E (G) - SC And the buyer makes the decision is to Max [G i, + E (G) - SC] If the house i is considered to have Gi < + E (G) - SC, the buyer will continue seeking for another house If G i + E (G) - SC, the buyer will decide to buy the house Therefore, the minimum benefit level of the house i for the buyer to buy, also called the G* purchase threshold, is determined by: G* Gi = + E (G) – SC With: After some necessary changes, we have: The above equation shows the optimal stop point Comparing optimal stop point equation in related theories, this stop rule is 23 affected by the charecteristics of the old house Then the author conlude propositions as follows: Proposition The relationship between purchase threshold and the charecteristics of the old house is determined: The affection of the characteristic of the old house on seeking behavior depend on the role of these charecteristics for the old house Proposition The trend in which the old home property's charecteristics tends to seeking time T of a home buyer depends on its role in the old home: Is shows that an unfavorable feature of an old home will have the effect of shortening the search time of a home buyer Thus, the analysis results of the theoretical model show that the properties of current dwelling (expressed in its benefit value, G0) have an impact on the buyer's current buying behavior 4.2 Empirically test the impact of current dwellings 4.2.1 Experimental testing method To test the theoretical impact of old home properties on the present searching behavior of homebuyers, the author will use a hedonic model to measure the effect of urban flooding of the prior house (Oldflood) on the transaction price and the time on market of the present choiced house of buyer 4.2.2 Experimental test results about the influence of homebuyers’ current dwellings on their current buying behavior Table 4.1: Estimation results of the prior house’s flooding feature on the transaction price and time on market of present house House price model Time on market model Model 12 Model 13 Model 14 Model 15 24 Variables Coef LnAge LnFloorarea LnSlotarea Shape Sun Widestreet Dstreet LnTworkpla LnTcbd Safe Waste Smelly Noisy Flooding Oldflood _cons D C Dummy R-squared Prob(F) Root MSE Dep Var N of obs Robust Coef Robust Coef Std Err Std Err -0.0387** 0.018 -0.0404** 0.018 -0.1208 0.1609*** 0.036 0.169*** 0.036 -0.1864 0.8548** 0.5282*** 0.056 0.5261*** 0.055 * -0.0864** 0.035 -0.0849** 0.035 0.0266 0.0539** 0.024 0.0564** 0.024 -0.1065 0.0189*** 0.002 0.019*** 0.002 0.0012 -0.0002** 0.000 -0.0002** 0.000 0.0006 -0.043** 0.022 -0.0439** 0.022 0.006 0.3738** -0.0709** 0.030 -0.069** 0.029 * 0.0129 0.0632 0.014 0.045 0.0125 0.0643 0.014 0.045 0.0357*** 0.010 0.0365*** 0.010 -0.0315*** 0.011 -0.0303*** 0.010 -0.0957** 0.042 -0.1021** 0.042 0.0631*** 0.023 5.5727*** 0.256 5.4939*** 0.258 Yes Yes 0.8867 0.23267 lnprice 448 0.8879 0.23427 lnprice 448 Robust Coef Robust Std Err Std Err 0.111 -0.1202 0.111 0.148 -0.1892 0.151 0.233 0.8555*** 0.233 0.169 0.0261 0.170 0.146 -0.1073 0.147 0.011 0.0011 0.011 0.000 0.0006 0.000 0.108 0.0063 0.108 0.144 0.3732*** -0.183*** 0.059 0.1829*** -0.17 0.156 -0.1704 -0.115*** 0.042 0.1153*** 0.1027** 0.046 0.1023** -0.2911 0.235 -0.2889 -0.0215 1.1945 1.450 1.2213 Yes Yes 0.3063 1.1973 lntom 448 0.145 0.059 0.156 0.043 0.046 0.237 0.163 1.485 0.3063 1.1987 lntom 448 Notes: - The models in the table are estimated by the method of OLS with strong standard errors - *, **, *** respectively represent significance levels at 10%, 5% and 1% respectively - Checking VIF variables in the model without multicollinearity signs (Source: Estimates based on survey data of the study) The price model results explain 89% and the time on market model explains 30%, similar to the studies cited The coefficients not change between the estimated models; it implies the adding of Oldflood variable is stability The models 13 and 15 show that the flooded of the prior house has a significant effect on the willingnes to pay of homebuyer However, the effect on time on market is negative (line with author's theoritical model) but insignificance The listing time is may not a good representation of the buyer's search time 25 This practical test result has contributed to advocating conclusions from the theoretical model of the effect of homebuyers’ old houses on their current purchase in section 4.1 Chapter 5: Conclusions and recommendations 5.1 Conclusion on the results of the thesis 5.1.1 Conclusions on measuring the impact of pricing strategies From the theoretical analysis of the correlation between the listing price, the selling price and the time on market, Cheng & et al (2008), Taylor (1999), Sun & Seiler (2013), two hypotheses are proposed: H0: A over-listing price strategy will signal a high selling threshold and thus increase the selling price of a house but at the same time prolong the time on market H1: An under-listing price strategy will be a signal to the buyer about the possibility of a problem with the house, and therefore the house will become harder to sell with a reduced price and a long listing period Based on survey data of 448 housing transactions in many districts of Ho Chi Minh City, the author has tested the impact of the pricing strategy on the selling price, for-sale time, and saleability based on a 3-step method: Step 1: the author has established a hedonic model of house prices from the properties of the house, this price becomes the basis for determining that the seller's bidding strategy (DOP) is the difference between the actual listing price and the expected market price Step 2: the author has measured the impact of this DOP listing price strategy on the selling price (due to the proximity between DOP and 26 selling price, the author then replaced this with the dummy variable Dum_DOP in this case) on Selling time is based on hedonic model with robust estimation The results show that the over-listing price strategy has the effect of increasing the selling price and shortening the time on market of houses Therefore, the author drew the conclusion of rejecting H and accepting H1 Step 3: Apply the Cox model with different time period to measure the impact of the listing price strategy on the probability of sale of the house and the fluctuation of this impact over the time of sale, this is one of new points of the thesis because no studies have investigated this issue The results show that the over-listing price strategy has the effect of increasing the probability of sale of houses, and is strongest in the first 30 days of sale, after that the effect diminishes and disappears after 180 days of sale 5.1.2 Conclusions on developing theoretical framework According to the author, the experiences that the buyers have their old houses will influence their current selection of new housing, but there is no theory to analyze this impact, so the author has developed a theoretical framework to analyze the impact of homebuyers’ old houses on their current buying behaviour, and this is one of the new contributions of the thesis The results develop a theoretical framework that concludes that the experience of home buyers with their old houses has an impact on their search for new housing In particular, buyers who with old low-benefit houses are more likely to find a new home because of the low threshold of benefit, they are willing to pay more and are expected to have a shorter search And 27 the empirical test results from 448 survey transactions also support this conclusion of the theoretical framework LIST OF PUBLISHED WORKS Nguyễn Thị Bích Hồng Trương Thành Hiệp, 2019 Kiểm định ảnh hưởng tâm lý kỳ thị hành vi người mua nhà: Nghiên cứu thị trường nhà Việt Nam Nghiên cứu Kinh tế, 2(489), trang 43 – 54 Nguyễn Thị Bích Hồng Trương Thành Hiệp, 2018 Lý thuyết tìm kiếm người mua nhà: Ảnh hưởng nhà lên định người mua nhà Tạp chí Tài chính-Marketing, 46, trang – Phan Đình Ngun, Tơ Thị Nhật Minh, Nguyễn Thị Bích Hồng, 2018 Thực trạng giải pháp phát triển thị trường bất động sản TP.Hồ Chí Minh Tạp chí Kinh tế Phát triển, 252(8), trang 30 – 38 Nguyen, H.T.B, Nguyen, H.T., Truong, H.T (2020) The role of Listing Price Strategies on the Probability of Sale of a House: Evidence from Vietnam Real Estate Management and Valuation, 28(2), 63 – 75 DOI: 10.1515/remav-2020-0016 ... Nguyễn Thị Bích Hồng Trương Thành Hiệp, 2019 Kiểm định ảnh hưởng tâm lý kỳ thị hành vi người mua nhà: Nghiên cứu thị trường nhà Việt Nam Nghiên cứu Kinh tế, 2(489), trang 43 – 54 Nguyễn Thị Bích... người mua nhà: Ảnh hưởng nhà lên định người mua nhà Tạp chí Tài chính-Marketing, 46, trang – Phan Đình Ngun, Tơ Thị Nhật Minh, Nguyễn Thị Bích Hồng, 2018 Thực trạng giải pháp phát triển thị trường. .. area and good environment are also factors that attract buyers and help shorten the time on market of the house Hình 3.3 The house price index in HCM 20 Nguồn: Savills Research and Consultancy