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Property markets are considered efficient when the market price of a transacted property equates with its market worth. If this condition holds then identical properties should sell or let for the same price. However, properties are heterogeneous, and information and operational constraints exist. Consequently, events in the transaction process and factors like time on the market, buyer and seller psychology and agent behaviour influence property prices, whereas in a perfectly efficient market they would have no impact. This gives rise to similar units selling for different prices. This paper examines the relationships between commercial property prices and time on the market for property. Tests fail to find evidence of a direct relationship between time on the market and transacted rents, time on the market and asking rents, and asking rents with transacted rents. The reason for the insignificant results could be because landlords would rather offer potential tenants non‐price incentives such as rent‐free periods, rent break clauses, shorter leases or fitting‐out costs to achieve a faster let than discount the agreed contractual rent. A more detailed examination of the physical, location and market conditions that determine the expected time on the market for a property to let is undertaken. Results suggest that the state of the property market is an important influence on the time it takes to let a property, and concurs with the evidence found in housing studies. With the support of our empirical findings and evidence from the housing market, we conclude that including measures of non‐price incentives, landlords’ motivation, tenants’ characteristics, and search costs in our model may explain the relationship more fully.

This paper aims to pose an important starting point for the application of the search-and-matching models to real estate appraisals, thus reducing the “gap” between practitioners and academicians. Due to relevant trading frictions, the search-and-matching framework has become the benchmark theoretical model of the housing market. Starting from the large related literature, this paper develops a simplified approach to modelling the frictions that focuses on the direct relationship between house price and market tightness (a common feature only for the labour market matching models). The characterization of the equilibrium through two main variables simplifies the analysis and allows using the theoretical model for empirical purposes, namely, the real estate appraisals.

This work is both theoretical and empirical. Theoretically, a long-run equilibrium model with a positive share of vacant houses and home seekers is determined along with price and market tightness. Also, the conditions of existence and uniqueness of the steady-state equilibrium are determined. Unlike most of the search-and-matching models in the housing literature, the out-of-the steady-state dynamics are also analyzed to show the stability of the equilibrium. Empirically, to show the usefulness of the theoretical model, a numerical simulation is performed. By using two readily available housing market data – the expected time on the market and the average number of trades – it is possible to determine the key variables of the model: price, market tightness and matching opportunities for both buyers and sellers. Although the numerical simulation concerns the Italian housing market, the proposed model is generally valid, being empirically applicable to all real estate markets characterized by non-negligible trading frictions. Indeed, the proposed model can be used to compare housing markets with different features (concerning the search and matching process), as well as analyse the same housing market in different time periods (because the efficiency of the search and matching process can change).

Several important results are obtained. First, the price adjustment – i.e. the difference between the actual selling price and the price obtained in an ideal situation of frictionless housing market – is remarkable. This means that the sign and the size of the price adjustment depend on the extent of trading frictions in the housing market. Precisely, the higher the trading frictions on the demand side (more buyers and less sellers), the higher the actual selling price (the price adjustment is positive), whereas the higher the trading frictions on the supply side (less buyers and more sellers), the lower the actual selling price (the price adjustment is negative). Accordingly, the real estate appraisers should assess the trading frictions in the housing market before determining the price adjustment. Second, an increase in the number of trades affects the house price only if the time on the market varies. Also, the higher the variation in the time on the market, the larger the house price adjustment. Indeed, the expected time on the market reflects the opportunities to matching for both parties and thus the trading frictions. If the time on the market increases (decreases), the seller will receive less (more) opportunities to match; thus, the actual selling price will be driven downwards (upwards).

As far as the authors are aware, none of the existing works in the search and matching literature has considered how to take advantage of this theoretical approach to estimate the house price in the presence of trading frictions in the housing market. Indeed, the proposed theoretical model may be a useful tool for real estate appraisers, as it is able to derive the trading frictions from the time on the market and the number of trades, thus estimating properly the house price.

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.

Using a difference-in-difference method, the authors estimate the impact that a hurricane has on the housing markets.

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.

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.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

Time on the market (TOM) has been widely tested in the US real estate literature using listing and selling data of houses captured in the multiple listing services (MLSs). Unfortunately in the UK there are no MLSs so it is not possible to undertake similar analyses. The approach adopted in this paper differs from traditional TOM analyses in that it focuses on the speed or time the market takes to correct for information differences between open market valuations and traded prices. In this context the paper introduces the concept of equilibrium time on the market (ETOM). The study therefore adopts a different approach to estimating TOM and in addition also examines the phenomenon within the UK commercial real estate sector. Based on a simple present value model, the time taken for the difference between an appraiser's estimate of open market value and known selling prices define our time on the market under equilibrium market conditions. Using the annualised UK Investment Property Databank all‐property total return index for a sample period of 17 years between 1983 and 1999, the average ETOM was estimated to be 8.4 months. This figure, however, varied and depended on market conditions.

Developing residential units is crucial in the socio-economic development of a country. The investor faces not only uncertain transaction price (price risk), but also uncertainties about the marketing period risk. Predicting when the incurred money is being realized is difficult because of the imperfect nature of the real estate market. Thus, the purpose of this study is to analyze the variables that explain the time on the market (TOM) of housing units, identifying the relationships in-between and the effects on TOM of residential properties.

Following a multi-stage sampling process, a random sample of 120 housing units was selected. Data were collected using a self-administered questionnaire. The questionnaire contained 57 variables that can affect TOM. Semi-structured interviews were conducted to confirm some of the data and information on residential units from the developers. Direct observations were conducted to verify certain physical attributes and, finally, they were comprehensively analyzed using quantitative analysis techniques in SPSS 16.0 Statistical package.

Results confirmed that lesser advertising prices, attractive environment, proximity to the city center and proper shape of lands reduce the TOM. Similarly, higher prices, longer distance to the city center and irregular shape of land increase the TOM. The results strengthen the necessity of a comfortable environment appropriate to live, probably with greenery or water bodies, which is a key influential factor that reduces the TOM in Sri Lanka.

wIn the Sri Lankan context, there are few contributions to the real estate literature in this regard. Many scholars have concentrated on physical and economic characteristics, whereas this research adds the environmental factors. Therefore, this research makes a significant contribution to the body of knowledge in this area, as it puts more attention on including several variables, as well as newly introduced variables as determinants. Consumers can apply the research findings to assess the relative importance of housing attributes and services which they perceive most valuable, and then to make their purchase decisions. The findings also contribute to the investigations of the behavior of housing attributes and enable knowing as to what factors are to be promoted and what to be omitted to gain a shorter TOM.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to use local-level time series data to examine the determinants of housing starts and the price elasticity of supply for the Aberdeen local housing market.

Seven time series models are used in the analysis. The basic model treats housing starts as a function of the changes of current and lagged house prices, interest rate and construction cost. The other six models which are extensions of the basic model include other variables like time on the market, planning constraints and future expectations.

It is found that the local variables – changes in house prices, time on the market, planning regulation, lagged stock and lagged and future housing starts – are the main factors that influence new residential construction in Aberdeen. None of the national variables is significant, confirming the importance of limiting housing market analysis to the local level. The price elasticity of supply estimated is in the range of 2.0 to 3.2 for housing starts and 0.01 to 0.02 for housing stock. These estimates are higher than most of the elasticities for the other UK local markets.

There is the need to better understand the supply of housing at the various local housing markets. Unfortunately, however, most housing supply studies use national data. Because national data are aggregation of local data, using national studies results for local markets may be uninformative. Also, the few existing local studies use typically cross-section data or at least time series over relatively short time spans. This paper makes an effort to use quarterly time series data over a 25-year period for a local market and also include a planning variable which is different from local markets and often ignored in national or regional studies.

The purpose of this paper is to investigate whether or not the offer price affects the transaction price and the number of days the property is on the market. Specifically, is it possible for the broker to use the offer price as an instrument for obtaining a higher transaction price?

To test the hypothesis the general hedonic model is used, where the deviation of the transaction price and expected price from the offer price is a function of time on the market.

The results indicate that a high offer price is more likely to result in a high ratio of transaction price to expected price compared to a low offer price.

However, the overall conclusion is affected by the state of the market, that is, whether the market is static, rising or falling.

The best selling strategy in a rising market seems to be set a high offer price compared to the expected sale price.

The main contribution is that the paper not only analyzes the relationship between offer and transaction price, but also its relationship to expected price. It also tests for the existence of spatial autocorrelation, which is unique in this type of study.

The role of list price is often discussed in a narrative describing sellers’ preferences or sellers’ price expectations. This paper aims to investigate a set of list price strategies that real estate brokers have available to influence the outcome of the sale, which may be many times self-serving.

By analyzing real estate brokers’ arguments on the choice of the list price level, a couple of hypotheses are formulated with regard to different expected outcomes that depend on the list price. This study empirically tests two hypotheses for the underlying incentives in the choice of list price from the real estate broker’s perspective: lower list price compared to market value leads to the higher sales price, lower list price compared to market value leads to a quicker sale. To investigate the two hypotheses, this paper adopts different methodological frameworks: H1 is tested by running a classical hedonic model, while H2 is tested through a duration model. This study further tests the hypotheses by splitting the full sample into two different price segments: above and below the median list price.

The results show that H1 is rejected for the full sample and for the two sub-samples. That is, contrary to the common narrative among brokers that underpricing leads to a higher sales price, underpricing lower sales price. H2, however, receives support for the full sample and for the two sub-samples. The latter result points to that brokers may be tempted to recommend a list price significantly below the expected selling price to minimize their effort while showing a high turnover of apartments.

Although there are a large number of previous studies analyzing list price strategies in the housing market, this paper is one of the few empirical studies that address the effect of list price choice level on auction outcomes of non-distressed housing sales.