This statistic presents the real estate websites that proved most popular among people who hunt for properties to purchase in the United Kingdom in 2015. One fourth of respondents said they would use all three websites: Rightmove, Zoopla and OnTheMarket. However, OnTheMarket only had 2.1 percent of respondents reporting they would use the site alone.

The Multiple Listing Service (MLS) Listing Software market is projected to expand dramatically in the coming years. The market was valued at USD 231.5 million in 2025 and is estimated to reach USD 638.3 million by 2033, exhibiting a CAGR of 9.1% during the forecast period. The growing adoption of cloud-based technologies, mobile devices, and big data analytics for real estate management is a key driver of the market's growth. Additionally, the increasing demand for efficient property management solutions to streamline workflows and enhance productivity is further fueling market expansion. The market is fragmented, with several key players offering a range of solutions. Some of the prominent companies in the MLS Listing Software market include Zillow, Realtor.com, Rightmove, Trulia, Redfin, Apartment Finder, HotPads, LoopNet, Apartments.com, Zoopla, Rent.com, Auction.com, and others. The market is characterized by intense competition, with vendors focusing on innovation and differentiation to gain a competitive edge. The adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is also expected to transform the market landscape. These technologies offer enhanced property search capabilities, personalized recommendations, and predictive analytics, enabling real estate professionals to make informed decisions and optimize their operations.

This dataset contains the data used in the study titled “Is hiding my first name enough? Using behavioural interventions to mitigate racial and gender discrimination in the rental housing market”. The data was collected from the London rental housing market between 2021 and 2022. Racial and gender biases are pervasive in housing markets. Males and ethnic minorities face discrimination in rental housing markets globally. The issue has been so pronounced that it regularly makes national and international headlines. In response to a racial discrimination lawsuit, Airbnb had to hide guests’ first names from rental hosts in Oregon, USA, starting in January 2022. Yet, there is little evidence that such measurement effectively counteracts racial and gender discrimination in housing markets. Despite some well-established theoretical models developed more than half a century ago and a wealth of empirical evidence accumulated over the last two decades, studies examining effective solutions to combat discrimination remain sparse especially in housing markets. Given the complexity of the products and services involved and the relatively low frequency of transactions, nuanced studies are needed to understand how implicit racial and gender biases influence letting decisions. This study investigates housing discrimination at the intersection where longstanding market behaviours meet the evolving insights of behavioural research. Although behavioural interventions have the potential to address both statistical and taste-based discrimination in the housing market, their successful implementation remains a challenge. Given the persistent biases and socio-economic dynamics in the housing market, interventions must be carefully tailored to the context. By collecting evidence from field experiments, this research aims to gain insights into how real-world behavioural interventions can be effectively designed and implemented. Our focus remains twofold: to develop a robust theoretical framework and to translate its insights into tangible, impactful policy recommendations, with the ultimate goal of fostering a more inclusive housing market.Although China has almost eliminated urban poverty, the total number of Chinese citizens in poverty remains at 82 million, most of which are rural residents. The development of rural finance is essential to preventing the country from undergoing further polarization because of the significant potential of such development to facilitate resource interflows between rural and urban markets and to support sustainable development in the agricultural sector. However, rural finance is the weakest point in China's financial systems. Rural households are more constrained than their urban counterparts in terms of financial product availability, consumer protection, and asset accumulation. The development of the rural financial system faces resistance from both the demand and the supply sides. The proposed project addresses this challenge by investigating the applications of a proven behavioural approach, namely, Libertarian Paternalism, in the development of rural financial systems in China. This approach promotes choice architectures to nudge people into optimal decisions without interfering with the freedom of choice. It has been rigorously tested and warmly received in the UK public policy domain. This approach also fits the political and cultural background in China, in which the central government needs to maintain a firm control over financial systems as the general public increasingly demands more freedom. Existing behavioural studies have been heavily reliant on laboratory experiments. Although the use of field studies has been increasing, empirical evidence from the developing world is limited. Meanwhile, the applications of behavioural insights in rural economic development in China remains an uncharted territory. Rural finance studies on the household level are limited; evidence on the role of psychological and social factors in rural households' financial decisions is scarce. The proposed project will bridge this gap in the literature. We carried out the experiment at the UK's largest online real estate portal and property website, www.rightmove.co.uk. In 2021, Rightmove had 208 million visits per month and a total of 692,000 properties listed at their website. Therefore, the platform gives us access to the largest available database of rental property listings in the country. We searched rental properties in Greater London Area that are advertised between December 2021 and April 2022. Only houses, flats and apartments are included. All listings are handled by letting agents. No private landlords are involved. Once a property was identified as eligible for the experiment, we sent a total of five applications to the letting agent, asking for a viewing appointment. The five applicants will be from different ethnic groups (i.e., one from each of the five groups) but of the same gender. The five emails were sent with at least 12 hours in between so that no suspicious of spamming might be raised. A total of 360 properties were selected, which gives a sample size of 1,800. The sample is evenly divided between the two gender groups and the five ethnic groups. Specifically, there are 360 observations in each ethnic group and 900 observations in each gender group.