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Listing Price, Time on Market, and Ultimate Selling Price: Causes and Effects of Listing Price Changes
Abstract
Information about price changes during a home's marketing period is typically missing from data used to investigate the listing price, selling price, and selling time relationship. This paper incorporates price revision information into the study of this relationship. Using a maximum-likelihood probit model, we examine the determinants of list price changes and find evidence consistent with the theory of pricing behavior under demand uncertainty. Homes most likely to undergo list price changes are those with high initial markups and vacant homes, while homes with unusual features are the least likely to experience a price revision. We also explore the impact of missing price change information on estimating a representative model of house price and market time. Our results suggest that mispricing the home in the initial listing is costly to the seller in both time and money. Homes with large percentage changes in list price take longer to sell and ultimately sell at lower prices. Copyright 2002 by the American Real Estate and Urban Economics Association.
... The information provided by most prior studies is on the asking prices that residential property owners can obtain, especially in countries like the US and UK, specifically on the relationship among asking price, time on market (TOM), and eventual sale price (Yavas & Yang, 1995;Knight, 2002;Orr, Dunse, & Martin, 2003;Brown & Foo, 2004;Allen et al., 2009;McGreal, Adair, Brown, & Webb, 2009;Gatzlaff & Liu, 2013;Beracha & Seiler, 2013;Han & Strange, 2014;Fitzgerald, David, McIntosh, & Barret, 2019;Saull, Baum, & Braesemann, 2020). ...
... In Nigeria, Olaleye, Ekemode, and Olapade (2015); Agboola and Scofield (2018); and Onwuanyi (2018) are related to studies of local extraction. Other areas that have been focused on include, high sale/over asking price (Gholi Khosravi, 2007;Velma, Waller, & Turnbull, 2019); institutional effect influencing seller's presale decision (Li & Chau, 2019); impact of property agent on property market (Fereidouni, 2012); causes and effect of asking price revision on the sale price and TOM (Knight, 2002); conflict of interest between property seller and agent (David, Kluger, & Miller, 1991); change of estate agent before the conclusion of sale's transaction (Daneshvary & Clauretie, 2013;Hayunga & Pace, 2016;Horowitz, 1992). Oloke, Olawale, and Oni (2017) and Onwuanyi (2018) had a similar direction in Nigeria. ...
... When setting their optimum list price, the seller needs to balance the potential marginal utility that comes from a higher price versus the marginal dis-utility of a longer TOM (Hayunga & Pace, 2016). Knight (2002) noted a review or change in the asking price of the listed property and found that 38.4% of the sampled listed properties were changed or reviewed before they were eventually sold. Could this result from the inaccuracy of appraisal or ambitious overpricing by the owners or absolute-dictate of the owner of listed properties? ...
This study provided an empirical framework that isolates the supply and demand perspectives in urban residential property transactions towards removing the information shield and asymmetry nature of property transaction information in Nigeria. A three-stage classical panel longitudinal design was adopted through feedback from completed transaction data on residential properties from the licensed estate agents. The study provided balanced and complete transaction information desired by the parties to any type of residential property transaction and bridged the wide gap between transaction expectation and actualization to prevent improper signalling in property transaction prescriptions. The study outcome generated vital indices and metrics for creating a complete property transaction database for the attraction of international professionals to participate in Nigeria’s urban property market transactions. This study is the first one in Nigeria to link the asking prices on sales and rental transactions in residential property transactions from the start to the conclusion.
... The choice of the initial listing price is not necessarily final. Usually, property sellers will set an initial asking price, then watch the market reaction and adjust the listing price in response to buyers' demand (Knight, 2002). Not all sellers follow the same pricing strategies. ...
... Research studies that do not examine price changes during the marketing period have an inherent weakness in explaining the relationship between price and TOM. This may be reason for the conflicting findings regarding the relationship among asking price, degree of overpricing (DOP) and TOM (Knight, 2002). ...
... Research shows that high-value properties take longer to sell because asking prices for higher-value properties are not revised as often as listing prices for low-value properties. Knight (2002) attributes the absence of asking price revision for high-value properties to the fact that they are traded in a thin market, so there is little chance for the seller to realize that the property is overpriced. In a thin market, the seller has less information to learn after a failed sale and therefore, less chance to do a price revision (Khezr, 2015). ...
Purpose
The purpose of this study is to investigate how the degree of overpricing (DOP) and other variables are associated with the time on the market (TOM) and the final selling price (SP) for residential properties in the Paphos urban area.
Design/methodology/approach
The hedonic pricing model was used to examine the association of TOM and SP with various factors. The association of the independent variable of DOP and other independent variables with the two dependent variables of TOM and SP were investigated via ordinary least squares (OLS) regression models. In the first set of models the dependent variable was TOM and in the second set of models the dependent variable was SP. A sample of N = 538 completed transactions from Q1 2008 to Q2 2019 was used to estimate the optimum DOP that a seller must apply on the current market value of a property in order to achieve highest SP price in the shortest TOM.
Findings
The results of this study also suggest that the degree of overpricing in thin and less transparent markets is higher than that in transparent markets with high property transaction volumes. In mature markets like the USA and the UK where the actual sold prices are published, the DOP is around 1.5% which is much lower than the 11% DOP identified in this study.
Practical implications
It was found that buyers are willing to pay more for the same house in a bigger plot than a bigger house in the same plot. The outcome is that smaller houses sell faster at a higher price per square meter than larger houses. Smaller houses are more affordable than larger houses.
Social implications
There is a large pool of buyers for smaller houses than bigger houses. Higher demand for smaller houses results in a higher price per square meter for smaller houses than the price per square meter for bigger houses. Respectively the TOM for smaller houses is shorter than the TOM for bigger houses.
Originality/value
The database used is unique, from an estate agent located in Paphos that managed to sell more than 27,000 properties in 20 years. This data set is the most accurate information for Cyprus' property transactions.
... After searching in the market and bargaining with buyers, the seller may gather some new information and learn more about the demand over time, which may result in a revision of the previous pricing (Merlo, Ortalo-Magné, & Rust, 2013;Merlo & Ortalo-Magné, 2004). Knight (2002) points out that listings with large percentage changes in list prices will take longer to sell, even at lower prices, which means that mispricing is costly to the seller. Therefore, studying the factors influencing sellers' price concessions is meaningful to understand the information asymmetry in the second-hand housing market and to find ways to improve the efficiency of the transaction. ...
... The initial price can be supplemented using additional information such as the seller's urgency, market changes, and obscured negative housing characteristics reflected by subsequent price adjustments. The frequency and the size of price concessions are functions of what has been learned that takes place during the marketing period (Lazear, 1986;Knight, 2002). After setting a high price, the seller will learn about the market reaction over time and revise the listing price downwards (De Wit & van der Klaauw, 2013;Merlo et al., 2013;Merlo & Ortalo-Magné, 2004). ...
... After setting a high price, the seller will learn about the market reaction over time and revise the listing price downwards (De Wit & van der Klaauw, 2013;Merlo et al., 2013;Merlo & Ortalo-Magné, 2004). Price concessions are common in the housing market, as evidenced in the study of Knight (2002): 38.4% of the properties sold underwent at least one price adjustment. Studies from the Netherlands and the United Kingdom suggest that 20%-40% of sellers in the housing market will modify the initial list price (Merlo & Ortalo-Magné, 2004;Herrin et al., 2004;De Wit & van der Klaauw, 2013). ...
The seller's price concession reflects the existence of asymmetric information in the second-hand housing market, and it is beneficial to study the influence of the broker's commission incentive source on the information transmission in the process of second-hand housing transactions. This study examined 310,332 transactions in 17 cities, using the spatio-temporal autoregressive model. Results mainly revealed price concession under the both-sides broker commission arrangement was 1.64% significantly lower than that under the buyer-side commission. The broker commission incentive has a moderating effect on the transmission of market information during transactions, whereas the both-sides commission leads to an increase in the effect of objective market conditions and reduces the difference of effects of individual factors on price concessions. An exogenous demand shock may lead to a change in the relationship between brokers' actions and sellers' pricing strategies. The implications will serve to improve the norms of and transparency in the second-hand housing transaction market, and resolve the disparity between the broker agent mode and commission incentive scheme.
... Further, the modelling of price change in most cases was either a by-product of the models, meaning that the dependent variable was not the price change but the final transaction or listing price. Although it is easy to miss some studies in the sea of real estate literature, the ones using price change as a dependent variable were carried out by Knight [28], Khezr [27], Verbrugge et al. [40], but only probit and regression models were employed. Additionally, Pérez-Rave et al. [36] argued that the predictive power of hedonic regression is not mature and is more suited to inference, simultaneously admitting that machine learning (ML) models possess drawbacks in explaining predictive power. ...
... The empirical findings provided by Huang and Palmquist [24], Knight [28], Anglin et al. [1], Herrin [23], Johnson et al. [26], Benefield et al. [6] and Verbrugge et al. [40] suggested that the initial price setup or the degree of overpricing can affect the price change. The idea here is that asset owners set an initial price too high with respect to other similar properties on the market and eventually have to reduce their price. ...
... Unfortunately, only a handful of papers have investigated the latter issue. The papers that attempted to estimate the probability of the price change were written by Knight [28], Khezr [27] and Verbrugge et al. [40]. However, while Verbrugge et al. [40] noted that the initial rent price, TOM and location were the most important variables in predicting rent price changes, the authors regrettably did not analyse the sales price. ...
As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.
... Therefore, the analysis of the literature on the factors that determine a higher TOM can help in the selection of the variables that can condition the difference between the asking and selling price. In reality, a fundamental determinant of the aforementioned difference is precisely the strategic behaviour of the broker who, in choosing the list price, logically assesses the macro-and microeconomic boundary conditions, knowing full well that an incorrect definition of the initial price is costly both in terms of selling time and the final price (Knight et al., 2002). The duration of a property on the market is therefore influenced by the skills of the brokers. ...
... In realtà un determinante fondamentale della citata differenza è proprio il comportamento strategico dell'intermediario che nella scelta del prezzo richiesto valuta logicamente le condizioni al contorno macro e microeconomiche, sapendo bene che una errata definizione del prezzo iniziale, risulta onerosa sia in termini di tempo di vendita del bene che di relativo prezzo finale. (Knight et al., 2002). La durata della permanenza di un immobile sul mercato è dunque influenzata dalle capacità dei broker. ...
The lack of transparency in the property market and the resulting difficulty in finding comparables to use in property valuations, very often forces evaluators to substitute the asking prices with the selling prices in the market approach. This alternative is now also accepted by case law but has the limitation of having to quantify, albeit very roughly, the correction to be made in relation to the probable spread between the asking prices, taken as a reference, and future selling prices. The importance of the asking prices to understand the market, is acknowledged in international literature which has mainly focused (starting from the analysis of the prices themselves and the time spent on the market), on the search for the best sales strategies, or on the measurement of the illiquidity of the property market. This study, in an innovative way, also on the basis of the relationships already proven, instead, attempts to interpret and measure the difference between asking and selling prices, in order to build a reference for the adjustments made to the former in estimation practice. The target is pursued through the construction of a multivariate analysis model on a sample taken over a 12- year interval in the city of Potenza, Italy. The analysis allowed to measure and interpret the marginal contribution that macro and microeconomic variables provide to the explanation of the spread under investigation. La mancanza di trasparenza nel mercato immobiliare e la conseguente difficoltà di rilevare utili comparabili da utilizzare nelle stime immobiliari costringe i periti, molto spesso, a sostituire nel procedimento diretto i prezzi di vendita con i prezzi richiesti. Si tratta di una alternativa ormai riconosciuta anche dalla giurisprudenza, ma che ha in sé il limite di dover quantificare seppur in modo molto approssimativo la correzione da apportare in relazione al probabile spread tra i prezzi richiesti, presi a riferimento, e i futuri prezzi di vendita. L’importanza dei prezzi richiesti per l’interpretazione del mercato è riconosciuta nella letteratura internazionale la quale si è prevalentemente concentrata, partendo dal- l’analisi di questi e del tempo sul mercato, sulla ricerca delle migliori strategie di vendita, o sulla misura della il- liquidità del mercato immobiliare. Questo lavoro, in modo originale, anche sulla base delle relazioni già di- mostrate, prova invece a interpretare e misurare la differenza tra prezzi richiesti e prezzi di vendita, al fine di costruire un riferimento per la correzione da apportare ai primi nella pratica estimativa. L’obiettivo è perseguito mediante la costruzione un modello di analisi multivariata su un campione rilevato su un intervallo di 12 anni nella città di Potenza. L’analisi ha consentito di misurare ed interpretare il contributo marginale che variabili macro e microeconomiche forniscono alla spiegazione dello spread indagato.
... The property marketing duration literature covers a wide array of topics including brokerage commissions (Zorn and Larsen, 1986), brokerage firm size (Yang and Yavas, 1995), sales price (Yavas and Yang, 1995), list price changes (Knight, 2002), and relative property size (Tumbull, Dombrow, and Sirmans, 2006). Within this body of literature, there is some agreement on the issue of joint determination of property price and selling time stemming from original works by Belkin, Hempel, andMcLeavey (1976) andMiller (1978). ...
... Excellent samples here include, but are not limited to. Knight (2002), Tumbull, Dombrow, andSirmans (2006), andTurnbull andDombrow (2007). ...
Miceli (1989), in a search for the optimal time to allow a broker to market property, posits that the principal (seller) may use the length of the listing contract to motivate the agent (listing broker) to better align incentives. Expanding slightly on Miceli, this work predicts that longer time allotted the broker to market residential property will decrease broker effort, resulting in lower search intensity and eventually a longer marketing span for property, ceteris paribus. This prediction is borne out across three empirical modeling methodologies commonly used in time-on-market studies.
... On the other hand, Taylor (1992) finds that longer time on the market can be seen as a negative signal by many buyers and thus may result in a lower sales price. Similarly, Knight (2002) finds that homes that had high listing prices and that experienced price cuts not only had longer time on the market but also were sold at lower prices compared to similar homes. Krainer (2001) theoretically show that sales price, time on the market, and transaction volume are all correlated, and one can observe either markets with high sales prices, short time on the market, and high transaction volume or the opposite 8 . ...
... Using micro-level data that includes the records of all offers on a house and consequent listing price changes, Merlo and Ortalo-Magné (2004) find that the listing price influences the arrival of offers, which ultimately determines time on the market and that longer time on the market leads to lower rate of potential buyers and higher probability of price revisions. Similar to the findings of Taylor (1992) and Knight (2002), they show that houses that stay longer in the market receive offers that are lower compared to the listing price. A relatively high initial listing price ultimately results in a higher sale price but this also means longer time on the market. ...
Purpose
Many studies have shown that the intensity and the number of hurricanes are likely to increase. This paper aims to look at the immediate effects of hurricanes on the time on the market, share of houses sold and percentage of houses with price cuts in the housing market using the metropolitan statistical area-level data in Florida.
Design/methodology/approach
Using a difference-in-difference method, the authors estimate the impact that a hurricane has on the housing markets.
Findings
The authors find that a hurricane has a positive and significant effect on the time on the market. A hurricane leads to a delay of the sale of a typical house in Florida by five days. The authors test for within-year seasonality and show that these effects change with seasonality of the housing market. Markets with seasonal housing prices tend to be affected more by hurricanes than those where housing prices are not seasonal. The authors also show that effects of a hurricane are transient and fade away in a few months. The results remain significant as the hurricane intensity changes.
Originality/value
This is the first study to look at the short-term effects of the hurricanes and how their effects vary based on seasonality of the markets.
... In fact, as shown from studies and literature, listing prices are capable of representing a fundamental aspect of the asset value formation process, specifically for their influence on selling processes and price prediction [68,69]. Furthermore, studies demonstrate the impact of listing prices on assets liquidity [70] and price spreads, represented by the difference between the listing price and selling price [71]. ...
... Thus, researchers, real estate companies and public administrations are used to study and analyze listing prices to perform market analyses and to estimate the property values. Even if it represents a key limitation of this study, it is worth mentioning that previous studies demonstrated that listing prices can be considered a proxy for transaction prices [79] and that they can influence the selling processes and prices prediction [68,69]. Moreover, the data sample consists in a set of property listings containing detailed information that in some cases are not complete: this is the case of the building construction time period and also of the EPC label. ...
The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.
... While Faller, Helbach, and Vater (2009) find an 8% premium on average for transacted prices above listed prices in North Rhine-Westphalia, Germany, those differences were not explained by observed housing characteristics. More generally, systematic mispricing of housing characteristics has been found to be very costly to the seller (Knight, 2002;Merlo and Ortalo-Magne, 2004), in line with theoretical models of seller behaviour (e.g. Knight, Sirmans, and Turnbull 1994). ...
... Zhao and Zheng (2000) studied that the optimal price of perishable products in a given inventory would decrease over time. In a B2C context, retailers may decide to adopt markdowns when products have higher initial prices and common characteristics (Knight, 2002). Kim et al. (2021) explored the impact of B2C secondary markets on social welfare using the video game industry as an example. ...
Dynamic decisions have become pivotal strategies for sellers and consumers in C2C secondhand product market. This study instroduces a two-stage pricing model and a consumer choice model, aiming to scrutinize the dynamic pricing behaviors of individual sellers and their subsequent impact on consumer choice. Leveraging data from the Xianyu platform, akin to eBay, we identify two fundamental dynamics that underscore successful pricing strategies. Seller pricing behaviors for secondhand products are subject to a number of influences, encompassing the duration since release, consumer engagement with product features, emotive descriptions, and feedback from market information. Our investigation unveils that sellers tend to recalibrate prices in the second phase if their secondhand products remain unsold during the initial phase, leveraging additional insights amassed during the first period. This adjustment is particularly pronounced if the price adjustment in the second phase is influenced by the time since the product's release. Moreover, the extra information gleaned during the initial phase affects the range of price adjustments. Notably, our research uncovers that consumers exhibit heightened attentiveness to product features and optimistic emotional descriptions in the search stage. Conversely, during the purchasing stage, product prices and the time since release emerge as more pivotal determinants. However, prevailing price adjustment practices predominantly center around markdowns or markups, with the time since release dampening purchasing inclinations. Our findings disclose that among these practices, only price markdowns hold the power to effectively stimulate purchase decisions, particularly when influenced by the duration since release. Based on these insights, we suggest that sellers should refrain from concentrating solely on pricing adjustments for their secondhand products. Instead, they would benefit from incorporating the additional market insights gained from the time since the product's release and the feedback from consumers to enhance the quality of dynamic pricing decisions.
... For properties listed and successfully sold on the MLS, we use two constructed variables to measure sellers' patience. First, since sellers who alter listing prices may be more impatient (Glower et al., 1998;Knight, 2002), we define sellers as impatient if the listing price was ever decreased during the listing period, and 0 otherwise. Second, we follow Barwick et al. (2017) and use the ratio between the listing price and the predicted sale price to measure sellers' patience. ...
This study focuses on legislative bans imposed on short‐term rentals (STRs) and evaluates their effects on long‐term rentals in Irvine, CA. We find that contract rental prices in the long‐term rental market decrease by 3.0% within approximately 2 years after the enforcement of STR ordinance. The results are primarily driven by the supply side for long‐term rentals. The decline in rents is more pronounced: (1) for long‐term rental units that have similar property characteristics as those listed through Airbnb and (2) for those located in geographic areas with greater Airbnb exposure before the ban was enforced.
... A thin market is characterized by few buyers and sellers with few transactions (Armstrong 2006). Artwork, antique collectables, and real estate are often traded in thin markets (Knight 2002). In contrast, a thick market has high trading volume in which prices equate supply and demand (Acemoglu 2007). ...
Prices respond to equate supply and demand. However, price-setting in low-volume or “thin” markets is a challenge as is determining which items to carry. We present an algorithm that takes into account a store’s fixed costs, the cost of goods sold, prices, and listing duration to determine the portfolio of items to maximize profits. Prices can then be assigned as a mark-up over cost. The usefulness of this approach is demonstrated by applying it to a store on eBay in which the seller needs to meet a profit threshold. The findings identify how sellers of unusual items can effectively determine which items to list and how to set price to reach profit goals.
... In fact, as known, listing price is frequently the only variable that stands for the value of the real estate asset that can be easily accessed (e.g. through real estate agencies and online real estate portals). Since some studies have underlined that they can be considered as a proxy for actual transaction prices (Curto and Fregonara, 2012;Malpezzi 2003in Stanley et al., 2016 and that they play an important role in price prediction (Knight, 2002 andHorowitz, 1992 in Barreca et al., 2021), Figure 2. Effects of EPC ratings on real estate prices in Europe: countries covered by recent peerreviewed articles. ...
Buildings’ energy efficiency may affect real estate prices, but the literature suggests that the effects of green attributes and Energy Performance Certificate ratings on the value of residential properties in Europe are still variable across contexts. The adoption of methods able to appropriately investigate this issue is thus essential. In this framework and to support future studies, this paper offers a methodological review of scientific works on the topic published in the last five years. Our work does not only represent an update of other reviews, but it originally analyses the papers by a methodological viewpoint. Results highlight a progressive refinement of the research questions and methods adopted. Then, the increasing importance of concepts such as latent variables and green attributes in the real estate pricing process is detected and identified as a field to be furtherly explored. Finally, Structural Equation Modelling is proposed as a promising approach for future studies.
... 14. Other studies, such as Knight (2002), Turnbull and Waller (2018), Bian et al., (2015), Johnson et al. (2015), and others, take a 2SLS or 3SLS approach. ...
Real estate agents play a critical role in reducing transaction costs in home sales. The incentives they face and the effect they have on selling price and time on market have been shown to differ depending on the legal setting governing the contractual relationship between principal (home owner) and agent. Using eight years of MLS data from a large Midwestern city, we study a market where the large majority of transactions involve a listing agent working directly with the seller and a cooperating agent working directly with the buyer. We find that more-active agents sell homes more quickly, but at a lower price. Important differences emerge when we separate agents’ roles into listing agents and selling agents. We find that recent market activity by listing agents leads to significantly lower sales prices and a quicker sale. An additional listing in the previous sixty days is associated with a 0.3 percent reduction in sales price and a 0.8 day decrease in days on market. More-active selling agents are associated with fewer days on market, but with no apparent impact on price. Relative to less-active agents, listing agents in the most active quintile are associated with an eight-percent lower transaction price and 14 fewer days on market.
... In a follow-up paper, Goodwin, Waller, and Weeks (2018) analyze text to determine the favorability of descriptive real estate terms. Finally, Knight (2002) exploits the comments section to identify "motivated" sellers. ...
This paper considers the information content of Multiple Listing Service (MLS) descriptions and employs a significantly larger data set than previous studies. The analysis first catalogs the most frequently used terms by real estate agents in MLS descriptions. Using hedonic modeling, we estimate the effect of this qualitative information on transaction price and days on the market. Finally, we extend earlier empirical work by utilizing our larger MLS data set to forecast the probability that a house will sell after it is listed. This last contribution further sheds light on the role of qualitative information to infer property condition or circumstances that surround the sale of the property.
... Usually, when time on market is included in a selling price estimation model, this variable tends to be statistically significant and negatively correlated to price. In other words, a longer time taken to sell a house means a lower selling price (see for e.g., Haag et al., 2000;Johnson et al., 2001;Knight, 2002). The opposite relationship between price and time on market is not that straightforward: some studies show that a high selling price leads to a longer selling time, while others show exactly the opposite (see for e.g., Jud et al., 1996;Rutherford et al., 2001;Anglin et al., 2003;Björklund et al. 2006;Wilhelmsson, 2008). ...
This paper empirically tests the effect of the number of bidders on the sale price of condominium apartments in Stockholm by using data gathered during 2010. The results show that the number of participants in a real estate auction plays a significant role in the final auctioned price. The average price per square meter paid by every extra bidder has an increasing but decelerating growth, starting with an approximate 4 percent increase when going from one to two bidders.
... In the end, the settled price is a comprehensive result of listing price from the bargaining power and commission rate (Yavas & Yang, 1995). Not only too high but setting a house at a price too low also affects its marketability (Anglin, Rutherford, & Springer, 2003), which means a lower price and longer time to settle (Knight, 2002). For the house buyers, it is a matter of signal extraction, a higher list price can be magnified for those with low predicted variance in the list price. ...
This paper identifies the main trends in real estate research on frequently cited documents on the Web of Science database for 1990–2019, using quantitative methods in document-citation analysis. From the co-citation among the most cited 44 documents, this research presents a two-dimension visual mapping structure of real estate research and its important fields globally. Using statistical analyses including (1) correlation analysis, (2) factor analysis, (3) multi-dimensional scaling, this study identifies nine research trends on reducing significance order: (1) performance and investment features of property, (2) house price – household income, consumption, and investment, (3) house price setting, (4) amenity in property valuation, (5) green factor in the property market, (6) housing discrimination and segregation, (7) urban development, (8) modelling for real estate subsector and (9) urban transformation in cities of China. A further two clusters are formed as housing and investment property with factors surrounding the two. The top five ranking journals from the cited papers are presented. Findings of the study contribute to providing insights on the multidisciplinary structure of real estate research using quantitative method of bibliometric technique. This is believed to be the first study presenting comprehensive aspects of real estate research using co-citation analysis.
... In the end, the settled price is a comprehensive result of listing price from the bargaining power and commission rate (Yavas & Yang, 1995). Not only too high but setting a house at a price too low also affects its marketability (Anglin, Rutherford, & Springer, 2003), which means a lower price and longer time to settle (Knight, 2002). For the house buyers, it is a matter of signal extraction, a higher list price can be magnified for those with low predicted variance in the list price. ...
... Studies of various listing and pricing strategies have always been a part of the brokerage literature. Cubbin (1974) highlighted the possibility that sellers could reduce list prices in response to previous failures to sell their homes, which is reinforced by Asabere and Huffman (1993) and, for grossly overpriced homes or vacant homes, by Knight (2002). On a related note, Sun and Ong (2014) argue that sellers will reduce their asking prices when there are more units for sale and when a comparable unit has recently sold, given a stock of highly substitutable housing units. ...
A co-listing strategy exists when two or more listing agents jointly represent the owner of a property who desires to sell it. This strategy is not new in the real estate brokerage industry, but its popularity has increased during recent years with the formation of teams of agents who repeatedly work together using the co-listing strategy. To date, the literature has not analyzed this business strategy. This study investigates the probability of selling, selling price, and time on market effects related to the use of the co-listing strategy. The results of this study indicate that co-listing is associated with a higher probability of sale, an increased selling price, and a marginal longer or shorter marketing time, depending on the situation. Comparing market outcomes for agents who form teams and repeatedly employ the co-listing strategy against market outcomes for agents who are involved in only one co-listing indicates that both types of co-listing produce higher prices and are more likely to result in a sale, but the effects for repeated co-listings are larger in magnitude. Additionally, repeated co-listing slightly reduces marketing time, but single co-listing slightly increases marketing time. The marketing time effects in both repeated and single co-listings, however, are too small to be economically important. In general, this study suggests that sellers are better served by the co-listing strategy compared to no co-listing, especially when the co-listing agents repeatedly work together in teams.
... Expert flips are rare, representing less than 1% of all transactions in our data. 9 For instance, Knight (2002) reports that the size of the price revision (that is, the list price-selling price gap) increases the TOM. 10 Taylor (1999) theoretically predicts this signaling effect, which is supported by the empirical evidence in Anglin et al. (2003). ...
We examine short term trades in the housing market over the period 2000–2013 using nationally representative data across multiple U.S. housing markets. Such trades, often characterized as “house flipping”, have gained currency in recent years with reality television shows depicting success and failure. We find evidence of returns in excess of market house price index growth (which we call alpha) during certain time periods with results that also vary across distressed versus non-distressed acquisition strategies.
... We note that one important modeling assumption in this paper is that we model price and market duration jointly as is the case in the real estate literature (Yavas & Yang, 1995;Huang & Palmquist, 2001;Knight, 2002;Turnbull & Dombrow, 2006;and Turnbull et al., 2013). The primary reason for jointly modeling these outcomes is that they are concurrently determined at the time of sale, which is important for estimating the effect on price and market duration from changes in nearby school quality (Zahirovic-Herbert & Turnbull, 2008), the impact from a sex offender moving into a neighborhood (Wentland et al., 2014), difference in agent characteristics (Turnbull & Dombrow, 2007), or the creation ...
Real estate research has primarily focused on examining aggregate shocks and dynamics to describe housing market trends; however, heterogeneity across housing types has been largely ignored especially on an intra-city level. In this paper, we address this heterogeneity through exploring how two different market types – single-family homes (SFH) and multi-family homes (MFH) within in a single metropolitan area – responded to the housing market bust and recovery between 2008 and 2016. Results from a robust series of specifications provide evidence that market dynamics of price, liquidity, and degree of overpricing deviated substantially both during the housing market bust and recovery period, and showed significant variation across housing types. These outcomes are novel in demonstrating that SFH and MFH are differentially affected by market shocks, and that a time-constant control for housing type may not accurately capture housing market dynamics in any single locale, potentially leading to erroneous conclusions about policy implementation.
Purpose
The paper employs a unique online user-generated housing search dataset and introduces a novel measure for housing demand, namely “contacts per listing” as explained by hedonic, geographic and socioeconomic variables.
Design/methodology/approach
The authors explore housing demand by employing an extensive Internet search dataset from a German housing market platform. The authors apply state-of-the-art artificial intelligence, the eXtreme Gradient Boosting, to quantify factors that lead an apartment to be in demand.
Findings
The authors compare the results to alternative parametric models and find evidence of the superiority of the nonparametric model. The authors use eXplainable artificial intelligence (XAI) techniques to show economic meanings and inferences of the results. The results suggest that hedonic, socioeconomic and spatial aspects influence search intensity. The authors further find differences in temporal dynamics and geographical variations.
Originality/value
To the best of the authors’ knowledge, it is the first study of its kind. The statistical model of housing search draws on insights from decision theory, AI and qualitative studies on housing search. The econometric approach employed is new as it considers standard regression models and an eXtreme Gradient Boosting (XGB or XGBoost) approach followed by a model-agnostic interpretation of the underlying effects.
This paper investigates a possibility of mispricing and withdrawal from housing market among potentially disadvantageous properties. A comparison of price setting and property characteristics between listed properties on real estate portal and transacted properties is conducted in the suburb of the Tokyo metropolitan area. We find that old properties that appear to be underpriced (possibly for their unobserved low housing quality) are not likely to be included in the transaction database, suggesting a possibility for owners to give up selling at their reservation price. We also show a tendency that such properties set excessive initial listing price, which will be largely discounted as their time on market gets long. Our results imply needs to encourage appropriate pricing and to enhance market transparency for successful transactions of potentially disadvantageous properties.
Recurrent list‐price reductions for a house may signal the impatience of sellers to conclude a sell transaction more quickly, leading to more visits and a higher likelihood of being sold (positive signal). Recurrent list‐price reductions may also provide a market signal that the listing is problematic and thus harder to sell without a list‐price reduction, leading to a lower likelihood of being sold (negative signal). Unlike standard survival analysis, we investigate which signal prevails using a joint frailty model that accounts for the interdependence among recurrent list‐price reductions and the association between the recurrent reductions and the sold event. Our novel data set contains the time‐dated recurrent list‐price reductions for each house listed on the market. The results from the joint frailty model show time‐varying negative impacts of list‐price reductions on the likelihood of a house sale, supporting the dominance of the negative signaling effects of recurrent list‐price reductions. Although listings with frequent list‐price reductions are less likely to be sold, sold houses sell at a higher ratio of sold price to last list price, which incorporates current market conditions and fairer pricing, holding constant the initial list price and the aggregate list‐price reduction from the initial list price. This article is protected by copyright. All rights reserved.
This study examines cash financing transaction price implications over a 12-year United States (U.S.) housing market cycle centred around the 2008 Housing Crisis, a period of unprecedented transaction price volatility. We establish theoretical reasoning and empirical confirmation of the mortgage contingency pricing effect, operationalised as a cash financing discount. We document that cash discounts are associated with market conditions, price levels, and improvement sizes and conditions. Larger empirical cash discounts are related to greater market distress, lower price levels, smaller improvement sizes, and inferior improvement conditions. We conclude that a one-size-fits-all rule-of-thumb is not appropriate when estimating cash financing pricing impacts. Finally, additional research is encouraged across different market conditions and in non-U.S. markets.
In Italy, the opacity of the real estate market, which often does not reveal the real consistency of selling prices, or, in relation to the phase of the economic cycle, the low number of transactions force appraisers to use asking prices as comparables in the market approach. The international literature recognizes the importance of analyzing the relationship between asking prices and selling prices or time on market for the interpretation of the real estate market. In this work, the analysis of the difference is aimed at interpreting its variance by identifying the variables that have greater weight. For this purpose, a multivariate analysis model is built on a sample of data over a 12 years interval recorded in the housing market of the city of Potenza, Italy.
We employ machine learning to develop measures of residential real estate uniqueness from written advertisements. These measures are exogenous from sale prices. We distinguish the effect of market uniqueness (comparing houses for sale at the same time) from the effect of universal uniqueness (comparing houses in the same sub-market) on sale prices and time on the market (TOM). The hedonic models show that a one-standard-deviation increase in market uniqueness leads to a 13% ($48,490) increase in sale prices at the cost of delaying the transaction for 1.7 days, whereas a one-standard-deviation increase in universal uniqueness leads to only an 11% ($41,030) increase in sale prices at the cost of delaying the transaction for 3 days. We validated the impact of uniqueness on TOM using two hazard models. Our results highlight the importance of uniqueness and market timing in real estate.
This paper studies how the announcements of fiscal law changes affect the real estate market, focusing on the case of Spain. An announcement of a future fiscal law change gives the opportunity to buyers to advance or delay purchases to maximize fiscal benefits. In particular, we study announcements and their posterior effects about the mortgage tax laws in 1998, 2010, 2011, and 2013 plus the VAT law in 2012. The paper is based on contextually rich data from 2004 through 2015 for Spain, provided by a real estate agent with a strong presence across the Spanish territory. We use two dependent variables to best capture the changes: time on market of a dwelling and the price discount of the dwelling. Simultaneity bias is avoided by considering that the degree of overpricing and atypicality affects time on market but not the selling price. The identification strategy is improved by considering the type of properties most affected by the changes versus the rest of properties, using a difference‐in‐difference estimation. We consider two tax policy announcements: income tax credit on dwelling purchases and VAT rate change on the purchases of new dwellings. In the case of the income tax credit, this fiscal policy affects only primary accommodations. In the case of the VAT tax rate, only new houses are affected. We show that credible fiscal policy announcements distort the housing market by temporarily decreasing dwellings’ time on market and their price discounts, to immediately and long‐lastingly increase them just after the tax policy expires. There is a negative causal effect of tax policy announcements on the housing market.
Policymakers grappling with how to meet EPA water quality standards within the Chesapeake Bay must weigh the cost of water quality restoration against the benefits accrued to nearby homeowners. Missing from this analysis are the benefits homeowners receive from increased home liquidity – or how quickly a home is sold once listed. In this paper, we exploit variation in water clarity data to examine its relationship with prices and liquidity using real estate data (2008–2015) from the Baltimore region. We find a one-foot improvement in Secchi depth, needed to meet the total maximum daily load recommendation for the central and northern portion of the Chesapeake Bay, increases housing prices by $9600, decreases time on market by 19.7 days, and reduces seller holding costs by $1280. This cost-savings is welfare-enhancing for many home sellers, especially absentee homeowners who are likely to net an additional 13.3% in water quality benefits when liquidity changes are considered alongside property value increases. Not accounting for the additional benefits and losses accrued through home liquidity suggests water quality restoration projects and damages from impairment will be undervalued.
The role of publicly available information in the price discovery of stock and bond markets has been extensively examined, while the study of its role in the residential real estate market has been scant. Supported by a theoretical framework and using a novel dataset from a third-party data provider and Automated Valuation Model (AVM) appraiser, Zillow, we show that public information available at the time of initial listing (as opposed to relying on ex-post realized transaction price) can help explain selling price, time-on-market, as well as the probability of listing price revisions.
We examine the iBuyers’ business model and their impact on housing markets. We find that iBuyers tend to enter neighborhoods that have more easily‐priced and homogeneous homes, as price discovery is simpler and more consistent with their pricing algorithm in those areas. iBuyers purchase homes at lower prices than individual owner‐occupiers, and this acquisition discount reflects the benefits iBuyers offer to motivated sellers rather than distressed home purchases or unobserved lower‐quality housing characteristics. Lastly, a greater presence of iBuyers results in a higher volume of local housing transactions and encourages more home sellers to sell without listing. This article is protected by copyright. All rights reserved
Some real estate investors engage in short-term trading in spite of the high transaction costs that this involves. While previous studies have identified various incentives that encourage short-term investors to engage in these practices, there has, to date, been little investigation into the influence of different market conditions over their performance. Based on real estate transaction data from Hong Kong, this study finds that buying and reselling within three months produces, on average, a gross return of 6% above the market. Three economic conditions are shown to be favorable to their performance: 1) comparable transactions are scant; 2) prices are more dispersed; and 3) market prices go down. Further analysis reveals that these short-term investors make a greater profit from purchases than from resales. While it is beyond the scope of this study to pin down the strategy adopted by each investor, the results are consistent with a “search” explanation, according to which, short-term investors behave as if they were arbitrageurs capable of exploiting the valuation spread between buyers and sellers.
Mortgage preapprovals have been commonly available for about 20 years. A buyer may benefit from a mortgage preapproval by increasing the likelihood of closing on the loan. A seller with an offer from a preapproved buyer is exposed to less risk of a transaction not closing and perhaps a quicker average time to closing. The findings show that commercial sellers are more likely to require preapprovals, especially for REO transactions. Time until the closing of a sale (TUS) is about 4.2% quicker for transactions with seller-required preapprovals. Time until the signing of a sales contract (TUC) is not less for seller-required preapprovals, but it is 15.3% quicker for REO preapproval sales. The selling price discount for preapproval properties averages 1.7%–3% for non-REOs and 3%–4% for REO properties.
Air pollution is one of the hazardous effects of urbanization. Hereby, one the most polluted cities in Ecuador is the Metropolitan District of Quito (DMQ). This study attempts to determine the marginal willingness to pay for a cleaner air in the DMQ using the impact of air pollutants on price properties. Spatial interpolation techniques visualized pollutant concentrations in the DMQ. Additionally, a hedonic price model estimated air pollution impact on properties. Results demonstrated hazard levels for at least three pollutants, being Particulate Matter PM 2.5 , Nitrogen Dioxide NO 2 , and Sulfur Dioxide SO 2 . Subsequently, the economic impact on the house market was statistically significant with a decrease in property value between 1.1% and 2.8%. These drop of value between 1,846.20 up to 4,984.74 US$ (United States Dollars) represents a substantial loss in property value for the DMQ and loss of revenues for the city.
Purpose
A common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices are decomposed into internal characteristics of an apartment, apartment characteristics and external characteristics. To account for the unobserved quality of the surrounding environment, price models include spatial price correlation factors, where the distance is usually measured as the distance in geographic space. In determining the price, a seller focuses not only on the observed and unobserved factors of the apartment and its environment but also on the prices of similar marketed objects that can be selected both by geographic proximity and by characteristics similarity. The purpose of this study is to show the latter point empirically.
Design/methodology/approach
This study uses an ensemble clustering approach to measure objects' proximity and test whether the proximity of objects in the property characteristics space along with spatial correlation explain the significant variation in prices.
Findings
In this paper, the pricing behaviour of sellers in a reselling market in Perm, Russia is studied. This study shows that the price transmission mechanism includes both geographic and characteristics spaces.
Practical implications
After testing on market data, the proposed framework for the distance construct could be used to obtain higher predictive power for price predictive models and construction of automated valuation services.
Originality/value
This study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces.
Projecting and monitoring NO2 pollutants' concentration is perhaps an efficient and effective technique to lower people's exposure, reducing the negative impact caused by this harmful atmospheric substance. However, many studies have been proposed to predict NO2 Machine learning (ML) algorithm using a diverse set of data, making the efficiency of such a model dependent on the data/feature used. This research installed and used data from 14 Internet of Things (IoT) emission sensors, combined with weather data from the UK meteorology department and traffic data from the department for transport for the corresponding time and location where the pollution sensors exist. This paper selected relevant features from the united data/feature set using Boruta Algorithm. Six out of the many features were identified as valuable features in the NO2 ML model development. The identified features are Ambient humidity, Ambient pressure, Ambient temperature, Days of the week, two-wheeled vehicles (counts), cars/taxis (counts). These six features were used to develop different ML models compared with the same ML model developed using all united data/features. For most ML models implemented, a performance improvement was developed using the features selected with Boruta Algorithm.
There are already countless articles on strategies to limit human exposure to particulate matter10 (PM10) pollution because of their disastrous impact on the environment and people's well-being in the United Kingdom (UK) and around the globe. Strategies such as imposing sanctions on places with higher levels of exposure, dissuading non-environmentally friendly vehicles, motivating bicycles for transportation, and encouraging the use of eco-friendly fuels in industries. All these methods are viable options but will take longer to implement. For this, efficient PM10 predictive machine learning is needed with the most impactful features/data identified. The predictive model will offer more strategic avoidance techniques to this lethal air pollutant, in addition to all other current efforts. However, the diversity of the existing data is a challenge. This paper solves this by (1) Bringing together numerous data sources into an Amazon web service big data platform and (2) Investigating which exact feature contributes best to building a high-performance PM10 machine learning predictive model. Examples of such data sources in this research include traffic information, pollution concentration information, geographical/built environment information, and meteorological information. Furthermore, this paper applied random forest in selecting the most impactful features due to its better performance over the decision tree Feature selection and XGBoost feature selection method. As part of the discovery from this research work, it is now clearly discovered that the height of buildings in a geographical area has a role in the dispersion of PM10.
Time is a critical factor or primary success metric in measuring the progress of construction projects since they
are normally time-bound. The construction industry, on the other hand, seldomly completes projects on time due
to its varied architecture - varying project styles, scopes, places, and sizes, as well as the participation of several
stakeholders from different disciplines. Building Information Modeling (BIM) is expected to be a valuable tool
in the construction industry, as it has the ability to mitigate construction project risks and complete projects
successfully. As such, a systematic review on the effects of BIM on construction project delays become vital.
Admittedly, systematic reviews provide a valuable opportunity for academics and practitioners to apply
established expertise to further action, policy or study. Using the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) guideline, this study thus aims to conduct a systematic review on the
effects of BIM on construction projects delay. This research approach yielded a positive effect of BIM on delay
across multiple regions of the world with different construction project types. This systemic review, as an
evidence-based methodology, will be crucial for the Architecture Engineering Construction (AEC) industry in
enforcing the adoption of BIM for current and future projects in the sector globally. It is recommended that a
comprehensive systematic review be conducted on other pertinent issues common to the construction industry.
The perpetual occurrence of a global phenomenon – delay in construction industry despite considerable mitigation
efforts remains a huge concern to its policy makers. Interestingly, this industry which produces massive amount
of data from IoT sensors, building information modelling etc., on most of its projects daily is slow in taking the
advantage of contemporary analysis method like machine learning (ML) which best explains factors that can
affect a phenomenon like delay based on its predictive capabilities haven been widely adopted across other sectors.
In this study therefore, a premise to compare the performance of machine learning algorithms for predicting delay
of construction projects was proposed. To begin, a study of the existing body of knowledge on the factors that
influence construction project delays was utilised to survey experts in order to obtain quantitative data. The
generated dataset was used to train twenty-seven machine learning algorithms in order to develop predictive
models. Results from the algorithm evaluation metrics: accuracy, balanced accuracy, Receiver Operating
Characteristic Curve (ROC AUC), and f1-score indeed proved Perceptron model as the top performant model
having achieved an accuracy, balanced accuracy, ROC AUC, and f1-score of 85%, 85%, 0.85 and 085 respectively
higher than the rest of the models and unachieved in any previous study in predicting construction projects delay.
Ultimately, this model can subsequently be integrated into construction information system to promote evidencebased decision-making, thereby enabling constructive project risk management initiatives in the industry.
The aim of the article is to present selected methods of duration analysis to assess the probability of exit from the real estate sale offer system, taking into account various types of competing risk (the year of submitting the property for sale). The study used the cumulative frequency function and the complement to unity of the Kaplan-Meier estimator. Using estimators, the authors compared the probability of withdrawing the real estate sale offer from the offer database due to: the sale of real estate and suspension or withdrawal of the offer. The analysis was carried out on the basis of data obtained from the West Pomeranian Association of Real Estate Brokers in Szczecin regarding the sale of residential real estate on the underdeveloped market—Szczecin. The survey is innovative because the calculation of the offer duration takes into account the properties that have been sold and are still current (on the day of the end of the survey). The probability of selling a residential property decreased significantly after 180 days in the MLS system. Apartments registered in 2018 and then in 2019 were the fastest to be sold. Due to a large number of censored observations, it was not possible to determine the survival function quartiles for 2017 and 2020.
In this paper, we propose a new approach to modeling a real estate price index in Morocco, based on the hedonic regression approach. The basic idea of this paper is to verify the importance of the characteristics of the real estate in the real estate price. Thus, based on data from the three major cities of the capital region of Morocco (RABAT Region), we estimated a hedonic model that takes into account spatial autocorrelation. The results obtained through this modeling generally confirm that the surface area and location of the real estate (land, house, villa and apartment) have a significant influence on the price of real estate.
Residential treatment centers offer the most intense form of treatment for substance abuse and are often embedded in residential neighborhoods. As a result of the Patient Protection and Affordable Care Act, the number of treatment centers has been forecasted to burgeon. We examine the external effect of residential rehab centers on nearby real estate. As addiction treatment centers are planned, a common response of nearby property owners is ‘‘not in my backyard’’ (NIMBY). Using a large MLS dataset from central Virginia, we estimate the impact of substance abuse treatment centers on nearby home prices and liquidity (as measured by time on market). We find that a neighboring treatment center is associated with an 8% reduction in nearby home prices, and that this discount is magnified for treatment centers that specifically treat opiate addiction (as much as 17%).
We propose a new assessed value approach to control for the amount of persistent unobserved quality. We apply our approach to a well‐established two‐stage framework developed by Genesove and Mayer (GM, 2001), who test the effect of an expected loss on final transaction prices in the housing market. We show that our assessed value model effectively mitigates the omitted variable bias and produces similar results as GM when the first‐stage residual is included. Importantly, our model does not rely on repeat sales and therefore provides a powerful new tool for estimating market value. Results are robust to alternative specifications, to controlling for loan‐to‐value ratios, to replacing final sale price with listing price, to alternative fixed effects, to subperiods, to different holding periods, to simulated quality, to excluding flippers, and to controlling improvements between sales.
This article is protected by copyright. All rights reserved
The existing real estate literature extensively documents the relationship between housing prices and school quality and, to a lesser extent, the effects of school quality on market liquidity. However, the capitalization and liquidity effects of new schools with unknown quality has been substantially understudied given the importance of understanding homebuyer responses to the opening of new schools. In this paper, we implement a novel three-stage least squares estimation framework to jointly examine the impact of newly opened elementary schools on housing prices and liquidity in Baltimore County, Maryland. The results provide strong evidence that homebuyers positively value these new schools through increases in prices and liquidity, despite their level of unknown quality, and the results are robust to alternative specifications and explanations. The outcomes of this research suggest future empirical work must address both price and liquidity concerns when determining the impacts of localized policies that shift school boundaries.
This article examines the usefulness of listing prices as leading indicators of house values and as predictors of the direction of housing markets. With Multiple Listing Service data from a large metropolitan area, we create two price indexes, using first listing price and then selling price as the dependent variable in the hedonic regressions. The market is then geographically and categorically segmented, and Granger causality tests are performed to analyze the leading aspect of list prices in the list price-sales price relationship. We find that different segments of the market perform quite differently over the time period of our study, suggesting that for data-based appraisal purposes care is needed in determining the manner and level of aggregation. We also find, however, that market list prices continue to convey important information about subsequent selling prices in most market segments.
The paper presents and estimates a simultaneous model of housing and real estate broker services markets. The multiple listing service (MLS) successfully separates broker listing and selling services and prevents faster-selling larger firms from exacting listing premia in the broker services market. Regardless of the incentives to withhold listings from the MLS, the results also imply that such withholding behavior is not pervasive.
Recently, real estate auctions have grown substantially in depressed markets. This article develops a framework to compare the performance of auctions to that of negotiated sales. First, the article solves a search model that incorporates unforeseen shocks and compares how prices respond in the short-run and long-run. Next, auctions are considered. Auctions are shown to obtain discounts because a quick sale results in a poorer "match" between house and buyer, on average, than could be obtained by waiting longer for a buyer. The model predicts that auction discounts should be larger in down markets with high vacancies, and in less dense markets.
A model of the single-family housing market is proposed in which households that move are both buyers and sellers. Households move when a stochastic process leaves them dissatisifed with their current unit. Household buyers expend costly search effort to find a better house, while sellers hold two units until a buyer is found. The vacancy rate, fixed in the short run, determines the expected length of sale and search, which play a central role in the reservation prices of buyer and seller. Market prices, the result of bargaining, lie between these two. The model yields a strong theoretical relationship (inverse) between vacancy and prices, which with competitive supply explains the existence of longer-run "structural" vacancy. Copyright 1990 by University of Chicago Press.
The conventional model of the housing market does not take into account the search process for a suitable housing unit. Based on a dynamic search theory, this paper develops and estimates a truncated regression model of the rental housing market with stochastic and unobserved truncation points. The author's model provides a joint estimation of the hedonic rice and the reservation rent equations. The results turn out to be superior to the ordinary least squares estimates of either the traditional housing demand function or the hedonic price equation. Copyright 1992 by MIT Press.
Lazear's "Clearance Sales" model is modified to incorporate the effects of the size of the population of potential customers on a seller's optimal price cutting strategy. The resulting theory is tested with data on pricing behavior in the residential housing market. It is shown that in markets for very expensive homes, where there are few potential buyers, prices are cut faster in the market for moderately priced homes. Further, in sales of new homes and homes with recent previous sales, where the seller likely possesses superior prior information about demand, prices tend to be cut more slowly.
We consider the role that seller motivation plays in determining selling time, list price and sale price. A new survey of home sellers suggests that sellers are heterogeneous in their motivation to sell. Our findings are that a seller who, at the time of listing, has a planned date to move sells more quickly than one who does not. Also, the shorter the planned time until a move at the time of listing, the shorter the actual duration of marketing time. We find that seller motivation affects sale price, but not the list-price markup. Our results suggest that theoretical models of the housing search process should be recast to allow for heterogeneous sellers. Copyright American Real Estate and Urban Economics Association.
In some housing markets, a seller may hire a broker to multiple list or exclusively list a property for sale or may bypass the brokerage industry and list the property privately as a "sale by owner." This article introduces a new model that illustrates the factors which will impact on the broker's and seller's preferred type of listing. An implication of the model is that if the choice is available, sellers and real estate brokers will employ a multiple listing service more often during slower market periods where the volume of sales is low and properties are more difficult to sell. An empirical analysis of Vancouver data yields results consistent with these arguments. Copyright American Real Estate and Urban Economics Association.
Using data collected from specially-designed questionnaires, the duration of search by a house buyer is estimated. Duration is measured in two ways: in terms of time and in terms of the number of houses seen. To explain this data, several features must be added to a simple model when search models are applied to a housing market. Many of the statistically significant variables, such as prior information and the quality of information provided by a newspaper or a real estate agent, deal with the provision of information. The type of agency that employs the agent and the characteristics of the buyer have little effect. Copyright American Real Estate and Urban Economics Association.
A two-stage least squares model of housing prices is estimated with data collected from 3358 single-family home transactions. The results provide evidence for an optimal marketing period and indicate that a liquidity premium is priced in single-family home sales. Consistent with the hypothesis derived from economic search models, the model shows higher selling prices for houses having longer expected marketing periods. The model also shows a price premium for houses that sell faster than expectations. This effect supports the concept that liquidity is a value-enhancing characteristic. Copyright American Real Estate and Urban Economics Association.
The seller of a real estate property and his broker have two primary goals: to sell the properly for as high a price as possible and as quickly as possible. While these are separate objectives, they are closely related through the listing price of the seller. The listing price affects how long it takes to find a buyer (i.e., Time On the Market = TOM), and TOM influences the price that results from the bargaining between the seller and the buyer. This leaves the seller and his agent with an important question: What is the optimal price to be asked for the property? The objective of this research is to provide a theoretical and empirical analysis of the impact of listing price on TOM and the transaction price. Copyright American Real Estate and Urban Economics Association.
The marketing of unique durable goods such as housing presents a good example for the application of search theory. An optimal stopping rule strategy is employed to model sellers' behavior. The primary hypothesis is that the greater the atypicality of a house, the greater the expected variance of offers. Because a maximizing seller will wish to entertain more offers the greater is the variance, the marketing time of atypical houses will be relatively longer than that of standard houses. Using a sample of resale houses, the empirical study uses a failure time model to confirm the hypothesis. Extensions are mentioned, including discussions of the role of the list price and the limitations of the standard hedonic regression approach when applied to housing. Copyright American Real Estate and Urban Economics Association.
We determine the mechanism that a rational, profit-maximizing seller would use to revise his reservation price for a heterogeneous or infrequently exchanged good. For instance, while one dimension of a home's quality may be easily determined in competitive markets (e.g., the valuation of floor size, location, etc.), other dimensions of quality may be idiosyncratic (unit specific) and unobservable by the seller (e.g., aesthetics of the home). Here, a seller of a new or infrequently exchanged housing unit may use sales success information to revise his expectation of the unit's market-determined value and hence revise his reservation price. The rational seller will, upon arrival of the first buyer inspecting the unit, determine a sequence of reservation prices for this and expected subsequent buyers. This price sequence falls for subsequent buyers and starts from a lower initial price if the first buyer arrives later than expected. Through this mechanism, we offer an explanation for price dispersion and vacancy durations in housing markets. While we explicitly model the real estate market here, this price revision mechanism is also applicable to rental markets, labor markets, used car markets, and other markets characterized by heterogeneity and infrequent sales. Copyright American Real Estate and Urban Economics Association.
This paper presents a model with rental housing vacancies in equilibrium. Because of the indivisibility and multi-dimensional heterogeneity of housing units, the housing market is thin. As a result, a typical household entering the market is willing to pay a premium for its most-preferred over its second most-preferred available (vacant) unit. This confers monopoly power on landlords, which they exploit by setting rents above costs. Free entry and exit force profits to zero, with vacancies as the equilibrating mechanism. A nice feature of the model is that housing vacancies are socially useful in expanding the choice set of entering households, though there is no presumption that the market vacancy rate is socially optimal. Thin markets are modeled by assuming an idiosyncratic component to households' tastes over housing units.
The positive and normative properties of the basic model, which assumes costless search, are investigated. Then the model is extended to treat costly search. Finally, directions in which the model could usefully be extended are discussed.
Factors such as relocation and financial distress motivate the seller of a single-family home to facilitate sale by posting a lower list price, communicating the motivations to the marketplace, or offering sales incentives to agents. Impacts of seller motivations on selling prices and marketing times are estimated using data for single-family homes sold in Arlington, Texas, from 1991 to 1993. Results show selling price discounts for houses with sellers who are either eager, motivated, or anxious, houses with sellers who have relocated, foreclosures, and vacant houses. Only foreclosure houses show the reduced marketing time expected for properties with motivated sellers. The results further suggest that the list price is the seller's primary mechanism for selling the property. Reducing the list price fosters faster sales at the sacrifice of the selling price. Copyright 1996 by Kluwer Academic Publishers
Houses are routinely sold at prices below, but rarely sold at prices above, their list price. List prices appear to be price ceilings that preclude the possibility of sales at higher prices. This paper presents a theory of sellers' behavior that explains why there are list prices in housing markets and why list prices are distinct from sellers' reservation prices. The theory forms the basis of an econometric model that has been estimated using data from the Baltimore, Maryland, area. The estimated model predicts sale and reservation prices conditional on list prices. The predictions of sale prices are considerably more accurate than those obtained from a standard hedonic price regression. The estimated model also explains why sellers may not be willing to reduce their list prices even after their houses have remained unsold for long periods of time. Copyright 1992 by John Wiley & Sons, Ltd.
This paper discusses the bias that results from using nonrandomly selected samples to estimate behavioral relationships as an ordinary specification error or "omitted variables" bias. A simple consistent two stage estimator is considered that enables analysts to utilize simple regression methods to estimate behavioral functions by least squares methods. The asymptotic distribution of the estimator is derived.
The inferences a prospective home buyer can make about the quality of a house from the amount of time it spends on the market
and the seller's optimal strategy in light of these inferences are investigated. Depending upon the information structure,
the seller may have an incentive to post an inordinately high initial price (in order to “dampen” the signal transmitted to
future prospective buyers) or an inordinately low initial price (in order to make an early sale and avoid consumer “herding”).
It is shown that the sellers of high-quality homes do best when inspection outcomes are publicly recorded and do worst when
inspection outcomes are not public and the price history is not observable. Costly inspections create more adverse selection
but deter consumer herding.
Sellers of new products are faced with having to guess demand conditions to set price appropriately. But sellers are able to adjustprice over time and to learn from past mistakes. Additionally, it is not necessary that all goods be sold with certainty. It is sometimes better to set a high price and to risk no sale. This process is modeledto explain retail pricing behavior and the time distribution of transactions. Prices start high and fall as a function of time on theshelf. The initial price and rate of decline can be predicted and depends on thinness of the market, the proportion of customers who are"window shoppers," and other observable characteristics. Copyright 1986 by American Economic Association.