Online Advertising and Real Estate sales: evidence from the Housing Market

The online advertising market has grown rapidly since the appearance of the first online advertisement in 1994 (Hollis, 2005). According to eMaketer’s forecast, the total global digital advertising spending will reach $602.25 billion in 2022 (eMarketer 2022b). In addition, an astounding 81.5% of China’s total advertising spending will focus on digital in 2022, making it the top country in terms of the extent and scale of digital advertising spending around the world (eMarketer 2022a).

Advertisements have long been recognized as a convenient and quick way for consumers to find information they need during their product consideration and purchase process (Nelson, 1974). Prior research shows that advertising could lead to outcomes, such as increased product awareness, site visits, purchase intention, and actual purchases and sales (Assmus et al., 1984; Bass et al., 2007; Blattberg & Jeuland, 1981; Drèze & Hussherr, 2003; Ilfeld & Winer, 2002; Sherman & Deighton, 2001). Although the effects of advertising are widely documented in the literature, the advertised products are mainly consumer goods (e.g., food, clothes, and other daily necessities). Relatively, there is limited research that examines the effect of online advertising on other types of goods, such as real estate assets in the information systems (IS) and marketing literature.

Unlike consumer goods that are intended for consumption by the average consumer, hence typically cheaper, real estate assets (e.g., residential and commercial properties/houses) have consumption returns, which involve long-term investments and high transaction costs (Varian, 2014). They cannot be purchased repeatedly in the short term and are geographically fixed and heterogenous (Krainer, 2001). Hence, real estate investing requires consumers to undergo more procedures, spend additional time and energy, and acquire more information (Georgiev et al., 2003). This longer and more complex procedure of buying houses may lead to more questions. Does online advertising affect buyers’ purchases? Why and how are the eventual sales of real estate assets so intricate? Specifically, buyers’ considerations and purchase decisions of consumer goods are mainly driven by their needs. However, the purchase of houses are driven less by consumption needs and more from an investment decision that has high financial risks and can have a lifelong impact on buyers. Unfortunately, the role of online advertising in the sales of real estate assets, such as new housing units, remains an under-studied, but important, issue and is a research gap that this study attempts to address.

The objective of the paper is two-fold. First, we aim to explore the impact of online advertising on real estate asset sales. Second, we further examine and validate the underlying mechanism and discover some factors that may alter the relationship between online advertising and real estate asset sales. In essence, our research questions are: how and to what extent does online advertising influence the housing market? To examine these, we collect data from a rich database about online advertising and local housing market information for cities in China. China provides an ideal context for us to conduct our research because it is now the world’s largest residential property market with the total sales of both new and second-hand houses forecasted to be $3.5 trillion in 2020 (Forbes, 2020). Based on rigorous identification strategies and estimation methods, we find evidence that online advertising increases the sales of new houses. This effect is stronger with lower housing prices, higher residential incomes, and lower-tier cities. Furthermore, we discover that this effect could be due to the fact that online advertising lowers housing prices, attracts more immigrants, and reduces emigration to form a potential larger group of buyers. Lastly, we also report that online advertising on the new house market will not have a spillover effect on the related second-hand housing market. The notable findings from this research, therefore, provide important theoretical contributions and offer practical implications to practitioners and policy makers.

Advertising is the dissemination of information used to promote products for consumers. Based on dissemination channels, advertising is classified into two categories: offline and online advertising. Offline advertising is non-Internet-based advertising, including television, radio, and print ads (Bayer et al., 2020), while online advertising uses the Internet to disseminate product information, such as online display advertising (Goldstein et al., 2014), search advertising (Lu & Yang, 2017), and e-mail advertising (Wattal et al., 2012). Advertising has many economic benefits, including increasing product sales, enhancing firm performance and value, and improving market efficiency, which received considerable research attention. Our research is closely related to two streams of literature: (1) the usage and (2) the mechanism of online advertising’s impacts on economic outcomes.

First, past studies explored the influence of online advertising usage on economic outcomes. For instance, Xu et al., (2014) captured dynamic interactions among advertisement clicks to study the effects of various online advertisements on purchase conversion for consumer electronics. Ahn et al., (2018) explored whether being exposed to an advertisement during a search could change consumers’ e-commerce search behaviors by conducting eye-tracking field experiments on clothing products. Sun et al., (2020) studied the effectiveness of two popular advertising tools, sponsored search and social media endorsement, in increasing traffic and sales for online sellers at a retail e-commerce platform. They also examined the differential effects for sellers with low and high reputations. Moreover, Sahni (2016) focused on spillover benefits and explored the impact of online ads on the advertiser’s competitors, reporting that ads increased the sales of advertised products but also reminded consumers of similar (non-advertised) goods. Additionally, online advertising can lead to ad repetition, which can annoy consumers and is researched by Goldstein et al., (2014) and Todri et al., (2020). Goldstein et al., (2014) discussed the economic and cognitive costs of these annoying display advertisements, while Todri et al., (2020) further proposed a hidden Markov model that investigated both the impact of display advertising on consumers’ purchase decisions and the potential of persistent display advertising to stimulate annoyance for consumers. Other researchers compared the impacts of online and offline advertising (Bayer et al., 2020; Goldfarb & Tucker, 2011b). Despite the abundant research on the impact of online advertising for different kinds of products, most of these IS and marketing studies only explored the economic effects of online advertising based on consumer goods, but overlooked other types of goods, such as houses. Given the significant difference between the two types of goods, the overlooked impact of online advertising on real estate assets remains a research gap that this study attempts to address.

Another stream of literature explored the mechanism of online advertising influencing economic outcomes. Some focused on the pricing design of online advertising (Feng & Xie, 2012; Hu et al., 2016). For instance, Feng & Xie (2012) proposed a performance-based advertising model and discovered that adopting this advertising scheme profoundly impacted one fundamental function of advertising (i.e., signaling product quality). Hu et al., (2016) compared the cost per click (CPC) with cost per action (CPA) pricing schema to study the economic impacts, such as purchase rates, profits, etc. Some also explored the design of keyword auction mechanisms (Du et al., 2017; Liu et al., 2010). Researchers studied the impact of weighting schemes and minimum-bid policies in keyword bidding on economic revenues (Liu et al., 2010; Du et al., 2017) further considered multiple keyword auctions and empirically examined the role of keyword categories and match types in advertising and financial performance, such as the conversion rate and product sales. Additionally, some studies focused on online advertising auctions (Decarolis et al., 2020), considering the moderating role of keyword competition (Ayanso & Karimi, 2015). Others investigated the relationship between organic result mechanisms and sponsored search advertising performance (Agarwal et al., 2015; Yang & Ghose, 2010). Moreover, there are some studies about marketing mechanisms, such as targeting (Chen & Stallaert, 2014), retargeting (Sahni et al., 2019), and advertising competition (Agarwal & Mukhopadhyay, 2016). Although the above studies uncovered various mechanisms of online advertising’s influence in the consumer goods market, these findings can not be directly applied to our research context because of the significant differences between the consumer goods market and the real estate market. Specifically, in contrast to the commodity market or manufactured consumer goods that are partially differentiated, the real estate markets are considered completely product-differentiated because of the local nature of houses and are closely related to urban economics (DiPasquale & Wheaton, 1996). Therefore, it is important to factor in regional characteristics and movements across metropolitans when investigating questions related to real estate economics. Our study focuses on a basket of factors on city characteristics and macroeconomics to exploit and unravel the contingency factors and underlying mechanism of online advertising’s impact on the real estate market.

The above results document robust evidence that online advertising may increase new house sales. Next, we conduct additional analyses to offer more research findings and insights.

By collecting and analyzing a rich data set on online advertising and housing markets, we identify notable findings. First, we find that online advertising on new houses increases the sales of these houses. This sales impact is stronger with lower new house prices, higher resident income, and lower-tier cities. Additionally, we unveil that this effect could be due to the processes that online advertising lowers new house prices, attracts more immigrants, and reduces emigrations to form a potential larger group of buyers. Finally, we discover that online advertising in the new housing market is unlikely to have spillover effects in the related second-hand housing market in terms of price or sales.

Our research findings have the following contributions. First, our research is one of the few in information systems and marketing literature to explore the relationship between online advertising and the real estate market. Prior studies mainly document the impacts of online advertising in consumer goods markets (Goldfarb & Tucker, 2011a; Gopinath et al., 2013; Manchanda et al., 2006; Taylor et al., 2013). However, they do not focus on real estate assets or houses. Our research addresses this gap and extends the information systems and marketing literature.

Second, we contribute to the information systems and marketing literature by examining and reporting several contingent factors for the sales impact of online advertising. We document that the advertising impact is not uniform but depends on important factors, such as housing price, resident income, and city tier. These contribute to the literature by adding more important insights.

Third, our study is the first to explore the underlying mechanism of the impact of online advertising on house sales, via aspects such as housing price, immigrants, and emigrants. More interestingly, unlike other studies validating the spillover effects of advertising in other markets (Erdem & Sun, 2002; Rutz & Bucklin, 2011; Sahni, 2016), our research posits that the spillover effect may not exist in the housing market. Thus, this contributes to relevant literature by adding different research findings and conclusions.

Finally, our research findings offer important implications to practitioners and policy makers. For instance, our findings reveal that online advertising is still impactful in driving sales, even in the real estate market, which involves a long and complex purchasing process. Thus, real estate developers, who seek to promote house sales, or government agencies, who intend to regulate house sales, can perhaps rely on online advertising as a tool, especially further combining other factors like housing price, resident income, and city tier. Moreover, the reported underlying mechanism of the sales impact of online advertising could help practitioners better understand the nature of online advertising in the housing market. The opened “black box” of online advertising also allows practitioners to better track and leverage the impact of online advertising.

In this research, we build upon the stream of literature on the usage and mechanisms of online advertising to investigate how online advertising affects the sales of new houses and explore potential contingent factors and underlying mechanisms in the real estate market, which differs from the widely studied consumer goods market in the literature.

Despite our notable findings and important contributions, we still acknowledge some limitations. First, we only have data on advertising impressions but were not able to collect advertising expenditure data. Thus, we could not provide a more direct estimation of the relationship between advertising expenditure and sales to offer “dollar-value” insights. Second, we did not have access to more granular data (e.g., clickstreams and behavioral data), which capture whether buyers observe or click on some advertisements and their subsequent decision making, which allows us to conduct more in-depth investigations to offer more insights. Overall, these limitations may serve as important angles for future research.