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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.

The main purpose of this paper is to explore the listing behaviours of agents and sellers. In particular, the paper analyzes listing prices and the predicting power of the house features described in advertisements, to improve their use in real estate valuations. In Italy, selling prices are not public information and therefore listing prices play a key role for market analyses and are used by real estate companies and appraisers for estimating house values.

A traditional hedonic model was used to measure the overall contribution to listing price of the characteristics described in advertisements. The analysis was performed both on houses put on the market by agents and on houses put on the market by sellers. Listing price distributions and their deviation from normality were analyzed. Furthermore, a hedonic analysis was performed, which consisted of two steps. First, the coefficient of determination for any characteristic was computed. Second, the overall contribution to the listing price of the characteristics described in advertisements was measured.

The analysis shows the presence of factors which affect listing prices and which are not revealed to buyers in real estate advertisements. On the other hand, the presence of characteristics that do not affect the listing price but are described in advertisements was also found. Furthermore, agents and sellers showed different behaviours. While the marginal contributions of each characteristic estimated on a sample of houses put on the market by agents were significant, the analysis reveals that listing prices of houses put on the market by sellers are not explained by the house features.

To the best of the authors’ knowledge, this is the first study to propose a hedonic approach to exploring the major determinants of listing prices of houses on sale on the Italian market. The listing behaviour of agents and sellers and the predicting power of the observable characteristics could address the use of listing prices in real estate valuations. At the same time, the potential presence of unobservable factors that affect the listing price could be a source of bias in estimating the value of houses.

The purpose of this paper is to examine the behavior of buyers and sellers in making housing decision and analyses the mechanisms of the seller‐buyer interaction affecting house sale prices.

The research methodology relies on a cross‐sectional telephone survey and the statistical analysis of housing transactions in Hong Kong.

The list price is unimportant to the formation of the sale price. Rather buyer‐seller interactions affect housing prices. The list price is positively related to the number of revisions, and the size of reduction, in the list price, and the list period, but negatively related to the sale‐to‐list‐price ratio. Overpriced properties trigger larger price reductions, noticeably, in the first round of negotiation, and stay on the market longer. Short negotiation periods and time‐till‐sale, and a sale at a marginal reduction in the list price is expected by market participants and conforms with the historical sales data. Hence, market expectations are generally fulfilled and support rationality in a steady market.

There are sample size limitations, which might bias the results and weaken the generalizability. The limited housing transactions may not be representative of the population at large.

When the market conditions are moderate, offering the property for sale at close to its current market value would determine the best possible selling price.

Telephone surveys on home buyer‐seller interactions and critical analysis of sale records are extremely rare in Hong Kong. The paper illustrates how, in times of moderate economic conditions and housing prices, the strategic negotiation process will rationally bring the selling price close to the market value price.

The prediction of Airbnb listing prices predominantly uses a set of amenity-driven features. Choosing an appropriate set of features from thousands of available amenity-driven features makes the prediction task difficult. This paper aims to propose a scalable, robust framework to predict listing prices of Airbnb units without using amenity-driven features.

The authors propose an artificial intelligence (AI)-based framework to predict Airbnb listing prices. The authors consider 75 thousand Airbnb listings from the five US cities with more than 1.9 million observations. The proposed framework integrates (i) feature screening, (ii) stacking that combines gradient boosting, bagging, random forest, (iii) particle swarm optimization and (iv) explainable AI to accomplish the research objective.

The key findings have three aspects – prediction accuracy, homogeneity and identification of best and least predictable cities. The proposed framework yields predictions of supreme precision. The predictability of listing prices varies significantly across cities. The listing prices are the best predictable for Boston and the least predictable for Chicago.

The framework and findings of the research can be leveraged by the hosts to determine rental prices and augment the service offerings by emphasizing key features, respectively.

Although individual components are known, the way they have been integrated into the proposed framework to derive a high-quality forecast of Airbnb listing prices is unique. It is scalable. The Airbnb listing price modeling literature rarely witnesses such a framework.

This paper aims to identify a wide array of utility-based attributes of Airbnb listings and measures the effects of these attributes on consumers’ valuation of Airbnb listings.

A hedonic price model was developed to test the effects of a group of utility-based attributes on the price of Airbnb listings, including the characteristics of Airbnb listings, attributes of hosts, reputation of listings and market competition. The authors examined attributes as they relate to the price of Airbnb listings and, therefore, estimated consumers’ willingness to pay for the specific attributes. The model was tested by using a dataset of 5,779 Airbnb listings managed by 4,602 hosts in 41 census tracts of Austin, Texas in the USA over a period from Airbnb’s launch in Texas up until November 2015.

The authors found that the functional characteristics of Airbnb listings were significantly associated to the price of the listings, and that three of five behavioral attributes of hosts were statistically significant. However, the effect of reputation of listings on the price of Airbnb listings was weak.

This study inspires what they call a factor-endowment valuation of Airbnb listings. It shows that the intrinsic attributes that an Airbnb listing endows are the primary source of consumer utilities, and thus consumer valuation of the listing is grounded on its functionality as an accommodation. This conclusion can shed light on the examination of competition between Airbnb and hotel accommodations that are built on the same or similar intrinsic attributes.

To promote the success of peer-to-peer accommodation, this study examines the effects of online host–guest interaction as well as the interaction's boundary conditions of listing price and reputation on listing popularity.

Using 330,686 data collected from Airbnb in the United States of America, the authors provide empirical evidence to answer whether social-oriented self-presentation and response rate influence listing popularity from the perspective of social exchange theory (SET). In addition, the authors investigate how these two kinds of online host–guest interactions work with listing price and reputation to influence listing popularity.

The results reveal the positive association between online host–guest interaction and listing popularity. Notably, the authors find that listing price strengthens but listing reputation weakens the positive effects of online host–guest interactions on listing popularity in peer-to-peer accommodation.

This study is the first attempt to adopt SET to explain the importance of online host–guest interactions in influencing listing popularity as well as examine the moderating role of listing price and reputation on the above relationship.

In this paper, the authors aim to test the assertion that options act as a substitute for short sales by allowing investors an alternative way to act on bearish sentiment. An empirical test of this assertion requires a researcher to observe both types of firm – those that weren’t short sale constrained, as well as those that were. The authors examine the ability of options to alleviate the short sales constraint directly – in an environment where the constraint is likely to differ across firms in a systematic fashion, namely the market for American Depository Receipts (ADRs).

The authors examine 190 option introductions on ADRs over the period of 1982 to 2006. The question of how ADRs are chosen for options listing, and whether those criteria differ from those found using purely domestic options, is addressed using logistic regressions. The authors use the event study methods of Brown and Warner to examine the price effect of the listing. They use OLS regression to identify determinants of the cumulative abnormal return upon option listing. Independent variables are those indicated by existing literature that examines option listing on domestic securities.

In an environment where the effective short sale constraint varies across firms, the authors find support for the contention that US option listings reduce the effect of the short sales constraint, providing relief for investors who have negative sentiment about the stock and are subject to a short sale constraint. However, it does not appear that option listing entities seek out companies for which short sale constraints are stronger.

The authors’ hypotheses are similar to those of Mayhew and Mihov and of Danielson and Sorescu, but the authors assert that the ADR market is a more robust environment in which to test the hypotheses. This is due to the potentially large variation in the effective short sale constraints that results from the differences in their underlying home market legal and regulatory environments. In addition to relative short interest and the change in relative short interest, this environment allows the authors to use indicator variables to directly test the ability of options to substitute for short sales.

The purpose of this paper is to provide a comprehensive analysis of dynamic pricing by Airbnb hosts.

This study uses attribute and sales information from 39,837 Airbnb listings and hotel data from 1,025 hotels across five markets to test different hypotheses which explore the extent to which Airbnb hosts use dynamic pricing and how their pricing strategies compare to those of hotels.

Airbnb is a unique and complex platform in terms of dynamic pricing where hosts make limited use of dynamic pricing strategies, especially as compared to hotels. Notwithstanding their limited use, hosts who own listings in high-demand leisure markets, manage entire places, manage more listings and have more experience vary prices the most.

This study identified a great need for Airbnb to encourage dynamic pricing among its hosts, but also warned of the potential perils of dynamic pricing in the sharing economy context. The findings also demonstrated challenges for hotel managers interested in actionable information related to Airbnb as a competitor.

This is the first Airbnb study to use a comprehensive set of data over a continuous period in multiple markets to look at a number of listing and host factors and determine their relation with dynamic pricing strategies.

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.

The purpose of this paper is twofold: to explore the distribution and pricing characteristics of Airbnb listings in Malta as at May 2019; and to develop a pricing model to determine the factors which have a statistically significant impact on price per night of listings.

A descriptive analysis of location and pricing of listings was undertaken via heat mapping techniques. A cross-sectional ordinary least squares (OLS) regression was run to determine the statistically significant variables.

Listings tend to cluster around not only in traditional tourist towns but also in rural areas which opens up new opportunities for tourist lodging. The Southern Harbour region was found to be the most expensive with the Gozo and Comino region being the least expensive. The coefficients of the pricing regression model were in line with a priori expectations.

The study is based on a cross-sectional data set and thus fails to account for seasonal changes in prices. Likewise, the use of an OLS regression without incorporating quantile regression methods or spatial autocorrelation econometric techniques is another limitation of this study.

The paper is one of the few related to sharing economy rental platforms, particularly in Malta. It is also the first study in Malta to develop a comprehensive pricing model to determine what affects a listing’s price per night and the extent to which certain factors do so.