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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.

The purpose of the study is to introduce modeling of common neighborhood amenities as packages, rather than as separate independent variables in a single model. Results from the standard modeling technique of including separate controls for each amenity are provided for comparison. A secondary purpose is to provide price and time‐on‐market implications for amenities in seasoned versus newly constructed properties.

Common neighborhood amenities are grouped according to the total amenity bundle offered by each neighborhood. Hedonic pricing, hazard modeling, and two‐stage least squares regression are used to estimate price and time‐on‐market impacts for six common amenities.

Neighborhood tennis courts, clubhouses, boating facilities, and golf courses, as well as several amenity packages, significantly impact property values. Valuation of particular amenity packages differs between newly constructed and seasoned homes. Time‐on‐market results are less convincing.

Neighborhood amenities considered separately can produce misleading results, so amenity packages should be included in future research. Specific numerical results would not apply to other markets and perhaps not to other time periods.

The study offers evidence regarding which neighborhood amenities are valued most highly in newly constructed properties, which is of interest to developers. The study also offers evidence on which amenities are valued more highly in seasoned properties, which is of interest to buyers due to concerns about re‐sale values.

The study offers the first grouping of neighborhood amenities into packages to more closely resemble the way buyers consider amenities during the purchase decision. The study is also the first comprehensive survey of commonly‐offered neighborhood amenities.

The purpose of this paper is to examine the effect of visual technology on the price discovery process in listings of residential properties in Singapore from 2015 to 2018.

The authors empirically model the effects of 360 virtual tours and drone video on four dimensions in price discovery – buyers’ arrival rate, sale probability, transaction prices and time-on-market – using a comprehensive data set for the residential properties in Singapore.

The analysis shows that the availability of virtual tours or drone video in a listing increases the arrival rate from potential buyers, the probability of a successful sale and the selling price. These findings are consistent with the hypothesis that technologically enhanced tools improve the quality of information and the marketability of property. However, listings with virtual tours tend to be associated with longer marketing time, which is consistent with the prediction of the information overload hypothesis.

This paper extends the housing and price discovery literature by examining how technologically enabled new information affects property transactions.

To the best of the authors’ knowledge, this is the first paper to consider the impact of drone video on property market outcome.

Researchers have not widely explored design-based factors that govern buildings’ physical properties and human–building interactions. This paper aims to understand the influence of design-related factors on the time-on-market (TOM) of listed houses and, consequently, study the effect of design features on the desirability of a given house.

This research analyzes a dataset of listed houses, provided by the REALTORS^® Association of Edmonton (RAE) and covers a period extending from January 2009 to August 2019, using Cox proportional-hazards regression model to identify building features that influence people purchasing decisions.

The research findings affirm the statistical insignificance of the price on the TOM compared to other design features, such as the construction method, the installed mechanical systems and cladding materials.

The data used in the analysis comes from a single North American region, i.e. Edmonton, Alberta, Canada. Also, the data provided by the RAE includes only records that involve a realtor.

The observations of the research presented in this paper influence the housing market players’ decisions about housing designs, mainly those concerned with building new residential dwellings such as speculative builders and designers.

The research novelty stems from two aspects: the medium used for analysis, i.e. Cox proportional-hazards regression model, which allows considering the listed-but-not-sold units and helps to eliminate the survivorship bias that leads to over-optimistic outcomes; and the assessment of design-related features which allows to understand people’s preferences in design alternatives.

The purpose of this paper is to determine if lender experience in disposing of repossessed single‐family houses in the local market is significantly related to the probability a property will sell. In addition, other factors that are significantly related to the market duration of repossessed houses are identified.

The Cox proportional hazard model is used to analyze transaction data for 2,099 single‐family houses in Dayton, Ohio. Title to each of these properties was obtained by lenders through foreclosure. The study period approximates the first three years of the subprime mortgage crisis in the USA: 2007‐2009.

The marketing efforts of lenders with more local property disposition experience are found to be superior to the efforts of less experienced lenders. The results also indicate that the selling rate function increased over the study period, and there is seasonality in the data which is consistent with lenders attempting to limit holding costs.

The study is limited to the experience of lenders in a single local market over a three year study period. Additional research to determine if similar results apply in other markets would be a valuable addition to the literature.

While foreclosure is not a desirable outcome for any of the parties involved in a mortgage loan, the paper's results offer a bit of good news for lenders. The results are consistent with organizational learning theory which posits that experience should enhance performance. Given predictions that the mortgage crisis has not yet run its full course, lenders' performance in disposing of repossessed houses is likely to continue to improve.

This is the first study to apply the proportional hazard model to the study of foreclosed houses. This technique offers an advantage over previously applied methodologies because it allows the researcher to include properties that lenders did not sell during the study period into the analysis. All previous efforts were limited to sold properties and this restriction may have biased the previous results.

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.

Investors often utilize brokers to assist them in property acquisitions. These brokers are compensated through a cooperative commission, or bonus, that is publicized on the listing service. The purpose of this paper is to determine the relationship between advertised compensation packages and selling price, time-on-market and listing characteristics.

To examine variables likely to influence earnings of the buyers' broker, this study utilizes multiple and logistic regressions. Given the range of prices found in the 196,276 listings, the data was sorted on listing price and then split into ten, approximately equal, deciles.

The explanatory power of models with cooperative commission as the dependent variable was highest in the lowest deciles with type of financing, size and distressed status being highly significant. When comparing list- to selling price the average was 96.1%. As cooperative commission increased, the higher priced parcels sold at a higher price relative to list price. This potentially justifies higher cooperative commissions or exemplifies the principal-agent problem where effort is based on potential earnings. Fixed bonuses were used predominately for parcels under $62,234, likely to provide a minimum earnings amount. However, surrounding the median, it seems they may differentiate a property.

This research provides insight for practitioners on the impact of different variables, including cooperative commissions, on sale price and time-on-market. For example, cooperative commission increased for properties in the outer deciles implying that agents may be compensating for suspected difficulty. Additionally, the seasonality findings imply that agents can determine when to list and when to provide a fixed bonus to solicit attention. Results also suggest that practitioners will find it beneficial to market at an appropriate price rather than list high to create negotiating room.

This paper follows only one paper that covered a similar topic. However, this paper uses twenty years of multi-unit property listings from a major US city from 1996 to 2015. The focus on multi-unit properties is an effort to focus on a more sophisticated group of buyers that may be more experienced and make decisions more rationally.

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.

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 purpose of this paper is to provide new insights into asset liquidity in direct commercial real estate investment in the UK. Transaction data provided by four institutional investors of commercial real estate are used to test for changes in asset liquidity as manifest in recorded times from price agreement to deal completion. Median times to completion by stage of the transaction are presented alongside industry estimates.

Stages of the transaction process are modelled and median times per stage calculated to track changes in asset liquidity over, and between, the two periods of the study (2000‐2002 and 2005‐2008). Real times to completion are considered in conjunction with estimated times compiled through interviews with senior level investment professionals. This paper applies the Wilcoxon rank‐sum test to determine the significance of variation in median times across the two study periods.

This paper provides empirical evidence that liquidity increased from 2000 to 2008. Median times from price agreement to completion decreased significantly (p=0.015) from 2000‐2002 to 2005‐2008, indicating an increase in asset liquidity in step with an overall increase in transaction volume. Furthermore, senior investment actors were found to persistently over‐estimate transaction efficiency and underestimate liquidity risk when acquiring and disposing of commercial properties.

This work offers new insights into the changing nature of asset liquidity over the last decade based on a limited number of transactions. Additional studies involving larger samples of transactions would provide still greater insight into commercial real estate liquidity dimensions.

The paper presents evidence of pro‐cyclicality; asset liquidity varies positively with overall transaction volumes, and investment actors were found to overestimate asset liquidity suggesting a persistent underestimation of liquidity risk.

This paper addresses a gap in the extant literature offering real time on market‐time to completion observations alongside investor estimates. Median times to completion have been modelled and presented, together with time estimates provided by industry experts. Also, for first time in real estate research, median times to completion are shown to shorten significantly in‐line with increasing transaction volumes.