Explore the vast range of titles available.

We compare house sales on a For-Sale-By-Owner (FSBO) platform to Multiple Listing Service (MLS) sales and find that FSBO precommission prices are no lower, but that FSBO is less effective in terms of time to sell and probability of a sale. We do not find direct evidence of the importance of network size as a reason for the lower effectiveness of FSBO. We do find evidence of endogenous platform differentiation: patient sellers use FSBO while patient buyers transact more often on the MLS (where they avoid patient sellers). We discuss the implications for platform competition, two-sided markets, and welfare.

The primary function of Multiple Listing Services (MLS) in housing markets is to disseminate information from listing brokers to other member brokers about houses listed for sale. This study examines the impacts of MLS-member information sharing intensity on housing market outcomes, with information sharing intensity measured as the average daily number of times MLS members view an individual house’s listing during its marketing period. We develop a theoretical model and derive the equilibrium. The model predicts that increased information sharing intensity leads to greater probability of sale, reduced time on market, and higher house prices. Analysis of data from 32,102 listing records validates the model’s propositions. We find that a one-unit increase in the average daily number of views of a house’s listing increases the probability of a successful transaction by 5.7%, increases selling price by 0.2%, and reduces marketing time by 1.6 days.

We examine the price differentials for homes sold through traditional agents compared to For-Sale-By-Owner (FSBO) sales for two geographic markets with data from January 2016 to July 2017. While revealing that the \"MLS premium\" no longer exists, we find that FSBOs sell for significantly lower prices than comparable home sales sold by agents and for prices below the average differential represented by the commission rate (6%), even after accounting for endogeneity of the FSBO variable. We find that the magnitude of the effect varies by geographic market as well as type of home. Our results have implications for assessing the value of a real estate agent when determining sales commissions.

The existence of the real estate brokerage industry is generally attributed to high transaction costs in real estate markets. Brokers are typically expected to market sellers’ properties, assist in contract negotiations, and coordinate the post-contract tasks necessary to close transactions. Presumably, brokers can perform these duties at lower cost than sellers. In addition to cost efficiencies, brokers may also impact market outcomes. Numerous researchers have investigated whether or not the use of brokers as well as various broker actions, broker characteristics, and broker/seller legal relationships affect market outcomes in the form of price and/or, time-on-the-market effects. We extend this line of research by considering price, time-on-market, and probability of sale effects in relation to four specific broker strategies: public open houses, broker open houses, MLS virtual tours, and MLS photographs. The results indicate positive relationships between these strategies and house prices and mixed relationships between these strategies and probability of sale and time-on-market.

This paper examines the impact of commissions on the likelihood that a property sells, the amount of time it takes to sell, and the sales price for a large set of properties in Greater Boston. A higher commissions associated with a higher likelihood of sale, a modest impact on the days on the market, and overall no effect on the sales price. It is possible that high commission agents realize lower sales prices to increase the likelihood of selling a property. This result would be consistent with agents not fully internalizing the interests of sellers, with high commission agents benefiting more from completing sales relative to obtaining higher prices for their clients. In the current real estate brokerage industry , prices that agents charge their clients may not be a signal of quality since commissions do not appear to be informative of the agent's impact on days on the market or the sales price. These results raise the question of how home sellers and buyers match with agents and each other, and the overall value of intermediation.

This paper investigates the relationship of property price and time-on-market to the use of property photo depictions in a multiple listing service. Empirical testing reveals that price as a function of photo depictions is increasing at a decreasing rate for both interior and exterior photos. Testing also reveals that time-on-market as a function of property photos is increasing at a decreasing rate for interior photos, but is not related to exterior photos. Results are sensitive to the number of photos allowed by the Multiple Listing Service. Overall, the results suggest that additional photographs increase price, while simultaneously lengthening property marketing duration, ceteris paribus.

Using new data on real estate listings, we provide new evidence that foreclosures have a causal effect on nearby house prices and disentangle the effect into two sources: competition and disamenities. We identify the causal effect by showing that sellers respond to new REO listings in the exact week of listing, not a week before and not a week after. We disentangle competition and disamenity effects by examining the spillover effect across various stages of the foreclosure process. We find that competition effects are important in all areas, but only find evidence for disamenity effects in high density, low price neighborhoods.

Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.

At the closing of 2022, in the B.C.S. market, as a whole, the average price of homes sold (721) in that year, came to$1,029,682 dollars, compared to $ 720,135 for the 395 homes sold in 2019, equal to +42.89% price increase and an 82.53% increase on number of homes sold. The average price of Condominiums sold (1,136) in 2022 came to$447,316 dollars, compared to $ 403,441 average for the 415 units sold in 2019, representing a +10.77% price increase and a +173.74% on number of units sold! 2022 B.C.S. Total Sales and Sales Per Market The analysis of Land sales also reflects a similar uptrend behavior: during 2022 there were 744 land units sold, with an average price of$283,316 dollars, compared to 2019 (498 units) sold, at an average unit price of $ 175,499. [...]the land sale price increased 61.43%, and the number of units sold were up 49.40%.

This is the first study to lend empirical support to anecdotal media reports that indicate that real property buyers pay price premiums based on property names. Using a standard hedonic price model, we explore the price effects of property names that include the terms “country” and “country club” within a neighborhood. Buyers assign a premium of 4.2% for the term “country” and an additional 5.1% for the term “country club” in the property name. Wealthier buyers tend to be the leaders in paying this price premium, although buyers are less willing to pay these premiums during recessionary times.