Gender homophily and rent premiums in platform-mediated shared housing: Evidence from Ziroom in urban China

Abstract

Chinese

Digital platforms are increasingly reshaping how housing is accessed, priced, and experienced in cities. Platform logics, characterized by data-driven processes and algorithmic pricing, can reflect and reinforce existing social inequalities. This study examines how gender intersects with platform dynamics to shape rental outcomes in Nanjing, China, a major metropolis of nearly 10 million residents. Drawing on a year of data from 373,964 bedroom listings on Ziroom, the country’s largest shared rental platform, the study tests whether the gender composition of roommates influences rents. Hedonic regressions controlling for unit, building, and neighborhood characteristics show that all-female-roommate units command a rent premium, suggesting gender homophily is priced as a valued amenity. When interaction terms with room status are included, results show no initial rent difference under “reserve,” but a clear premium for all-female units under two leasing conditions: immediate move-in (“sign”) and subleasing (“sublease”). These findings suggest that the premium does not arise from platform-set base rents but from gendered preferences interacting with algorithmic pricing, whereby units with stronger demand are discounted less. This study contributes to research on platform-mediated housing, PropTech, and housing inequality by quantifying how platform systems reflect and reproduce gendered disadvantage. It also highlights the value of platform data for understanding emerging rental markets and calls for greater attention to gender in the governance of shared and platform-mediated housing.

Keywords

algorithmic pricing China gender housing inequality platform renting PropTech shared housing

Introduction

Traditional housing careers are undergoing transformations. Globally, young people are increasingly becoming “generation rent” (Hoolachan et al., 2017) and “generation share” (Maalsen, 2020). Rising housing costs and delayed family formation have led to smaller and single-person households (Buzar et al., 2007; Steinführer and Haase, 2009; Wall, 1989). For instance, the proportion of U.S. young adults living in married-couple households has halved in five decades (Jeffers et al., 2024). In China, the proportion of one-person households more than doubled from 6.3% in 1982 to 13.7% in 2010 (Shen et al., 2021), increasing demand for flexible options like shared housing. Globally, shared rental has thus emerged as a key strategy for accessing urban housing under affordability constraints (Harten and Boeing, 2024; Nasreen et al., 2024).

Meanwhile, digital platforms are reshaping how housing is accessed and experienced. Since the 2008 financial crisis, property technology (PropTech) has expanded rapidly, facilitating information provision, transactions, and property management (Baum, 2017). Within PropTech, “platform real estate” refers to digital intermediaries that manage properties through listings, algorithms, and automated services, introducing new dynamics such as platform logic, digital labor, and financialization (Fields and Rogers, 2021). Prior research shows that these systems co-produce housing value through complex and sometimes conflicting regimes of value (Fields and Rogers, 2021; Maalsen et al., 2024; Nethercote, 2023; Rogers, 2016; Sadowski, 2019) and reinforce pre-existing inequalities, including segregation and capitalist dynamics (Boeing, 2020; Faxon et al., 2024; Migozzi, 2024; Wolifson et al., 2024). Much of this work focuses on race, class, and postcolonial legacies, yet gender, an equally important dimension of structural inequality, remains underexamined in platform-mediated housing.

Addressing this gap requires examining how platform infrastructures and tenant data reshape rental markets as sites of value extraction (Rogers et al., 2024). As in other social domains, housing experiences within these platforms are gendered. Gender homophily, the tendency for people to associate with others of the same gender, is widely documented across social, recreational, and organizational settings (Caetano and Maheshri, 2016; Kleinbaum et al., 2011; Laniado et al., 2016; Ridgeway and Correll, 2004). For women in particular, such preferences are often reinforced by safety concerns and prior experiences of harassment (Liu et al., 2014; Xu and Zhang, 2022).

This study investigates how these gender preferences are embedded in and translated into rental prices in platform-mediated housing in Nanjing, a major Chinese city with nearly 10 million residents. Building on research on gendered rental practices (Harten, 2020) and on the use of platform housing data to reveal informal market dynamics (Harten et al., 2021; Nasreen et al., 2024), this study positions bedroom-level pricing on Ziroom, a formalized rental platform, within this framework to show how digital mediation shapes what becomes visible, comparable, and ultimately priceable for women in shared renting.

Rental pricing in platform-mediated markets is often governed by dynamic, algorithm-driven systems that use real-time supply and demand data (Ullah et al., 2018). While such systems appear neutral, they may embed or amplify unequal dynamics. Although algorithmic inequality has been documented in sectors like that of ride hailing (Bokányi and Hannák, 2020; Brown, 2023; Tang et al., 2021), less is known about how these mechanisms interact with gender in shared housing.

To examine how gender shapes pricing in platform-mediated shared housing, I employ hedonic price modeling, using roommate gender composition as a key explanatory variable. This approach estimates its marginal effect on rent, controlling for room, building, and neighborhood factors. Leveraging web-scraped data from Ziroom, China’s largest shared housing platform, I analyze over 370,000 listings with detailed information on housing features, including roommate gender. Building on evidence of gendered disadvantages in other housing markets (Harten, 2020; O’Connor et al., 2018) and broader theories of gender inequality (Akerlof and Kranton, 2000; Bloch, 1987; Charles et al., 2025; Maccoby, 1998; Thébaud et al., 2021; West and Zimmerman, 1987), I test whether all-female-roommate units command rent premiums, even when other attributes are held constant, and examine what platform mechanisms contribute to these differentials. The analysis demonstrates that platform-mediated pricing translates gendered preferences into rent differentials through algorithmic adjustments based on supply and demand thereby, monetizing and amplifying existing inequalities and disproportionately shifting cost burdens onto women tenants.

The next section reviews the literature on platform real estate, rental inequalities, and platform renting in China, followed by a conceptual framework that positions gender homophily as a socially produced and priced amenity in platform-mediated systems. Subsequent sections describe the data and methods, present the results, and discuss the conclusions and implications for housing platform governance.

Platform real estate and rental market transformations

Platform real estate and rental inequalities

Technological innovation has reshaped the real estate rental market over the last decade through PropTech, defined as the adoption of digital tools for data management, transactions, and property operations (Baum, 2017). These technologies streamline key processes, from marketing and leasing to maintenance. However, their value is not fixed but is shaped by intersecting and often conflicting regimes of value (Maalsen et al., 2024).

Within PropTech, platform real estate refers to digital intermediaries that facilitate property or rental transactions and interactions among stakeholders (Fields and Rogers, 2021; Shaw, 2020). Prior studies highlight key features such as data intensiveness, digital labor, and financialization (Fields and Rogers, 2021; Rogers, 2016). Companies like Zillow and Zoopla aggregate property listings, process them, and provide analytics, relying on data as a core form of capital (Sadowski, 2019). Their profits largely derive from collecting, analyzing, and monetizing data, often alongside other revenue streams (Rogers, 2016; Sadowski, 2019; Zillow Group, Inc, 2023). Extending these dynamics to rental markets, platform-mediated rental markets are increasingly structured by how platform infrastructures and tenant data reorganize access to housing and enable new forms of value extraction (Rogers et al., 2024).

While platforms are often promoted as efficient, research shows they reproduce entrenched inequalities (Boeing, 2020; Ferreri and Sanyal, 2022; Hess et al., 2021; Migozzi, 2024). Listings reflect socio-spatial inequalities shaped by historical segregation and digital divides. For example, Craigslist listings overrepresent whiter and more affluent neighborhoods, exacerbating information asymmetries and reinforcing residential sorting (Boeing, 2020). Data analytics and algorithmic sorting can further legitimize and intensify exclusion by enabling discriminatory tenant selection (Ferreri and Sanyal, 2022; Migozzi, 2024).

Taken together, digitalization not only transforms housing markets globally but also reinforces existing inequalities rooted in local property relations and socio-spatial divisions (Faxon et al., 2024). Empirical research on platform-mediated rental markets in China remains limited, with a few exceptions (e.g. Harten, 2020; Zhang et al., 2023). This gap is particularly important given the rapid expansion of platform-based renting in Chinese cities.

Platformization of rental housing in China

China’s rental housing market has expanded rapidly since 2010, driven by policy shifts and growing investment. Prior to 2008, homeownership dominated as the social norm (Zhang et al., 2021), but worsening housing affordability in large cities prompted a policy shift. Historically, the rental sector has been highly informal, with transactions mediated through personal networks or small-scale landlords (Harten et al., 2021; Wu, 2016). In 2015, the Ministry of Housing and Urban-Rural Development (MOHURD) promoted rental housing development in response to affordability pressures and increasing urban migration (MOHURD, 2015). These policy shifts, together with investor interest in formalizing private rental markets, enabled the rise of professional rental management firms such as Ziroom, UOKO, and Qingke (Ba and Yang, 2016; Zhang et al., 2023), marking a shift toward a more formalized and platform-mediated rental sector.

Rising expectations among younger generations have further accelerated the expansion of platform renting in Chinese cities (Ba and Yang, 2016). Although over 90% of rentals are still provided by individual property owners (Net Cultural Industry Research Institute and Ziroom Research Institute, 2025), many units are outdated and poorly equipped. Younger renters increasingly seek flexible leases and are willing to pay for higher-quality housing and better services (Yu et al., 2018). Platform-mediated rentals have emerged as appealing alternatives, offering furnished units, standardized management, and greater convenience. These platforms also address long-standing issues such as fake listings, information asymmetry, and high agency fees (Wang, 2018), aligning with the needs of the post-1980s migrants and recent graduates priced out of homeownership (Ba and Yang, 2016).

Ziroom, the focus of this study, became China’s largest platform rental provider by mid-2019 (Cogné, 2020), managing nearly 1 million units and facilitating over 50 million tenant transactions by 2024 (Net Cultural Industry Research Institute and Ziroom Research Institute, 2025). Ziroom leases units from private owners, renovates them, and sublets them through its app and website (Wang, 2018). Its integrated system for search, contracts, payments, and maintenance attracts young, tech-savvy tenants despite higher rents than comparable individually managed units.

Mirroring global trends, Chinese rental platforms rely heavily on data and algorithmic systems. Pricing adjusts dynamically based on supply and demand, maximizing profits while reducing reliance on traditional intermediaries (Cogné, 2020). With nearly 260 million renters, platform renting is poised for continued growth in China, reflecting both diversifying demand and increasing influence in urban housing (Net Cultural Industry Research Institute and Ziroom Research Institute, 2025). Although these systems appear efficient and user-friendly, their implications for housing accessibility and inequity remain underexplored.

This process of platformization and formalization is central to this study, as it enables new forms of information visibility, filtering, and preference articulation in rental markets. In doing so, it reshapes how housing needs are expressed, compared, and incorporated into pricing mechanisms. As digital platforms expand, their role in shaping tenants’ experiences and rental outcomes in China’s urban market warrants closer investigation.

Pricing gendered preferences in platform renting

This study examines platform renting through a gender lens. In platform-mediated housing, market outcomes are shaped not only by physical and locational attributes, but also by socially embedded needs (Harten, 2020). As housing transactions become increasingly mediated by digital platforms, gendered needs, preferences, and even disadvantages can be made visible, structured, and incorporated into market processes.

Gender homophily and gendered needs

Gender homophily, the tendency for same-gender individuals to associate, is widely observed in social media and networks, recreational activities, and workplaces (Caetano and Maheshri, 2016; Kleinbaum et al., 2011; Laniado et al., 2016). These patterns are not innate but socially constructed through gender norms and expectations from an early age (Basu et al., 2017; West and Zimmerman, 1987). Repeated exposure to gender-typical activities, roles, and behaviors shapes preferences and social skills, reinforcing gender divisions in adulthood and contributing to broader gender gaps in interests and opportunities (Charles et al., 2025; Maccoby, 1998; Ridgeway and Correll, 2004).

For women in particular, internalized expectations around appearance, demeanor, and career choices influence both everyday preferences and long-term life trajectories, creating invisible barriers and social penalties that limit access to opportunities and resources (Akerlof and Kranton, 2000; Forbes et al., 2007; Thébaud et al., 2021; Wolf, 2013). These norms extend into housing, where women tend to prefer same-gender living arrangements. Harten (2020) shows that in Shanghai’s informal rental market, women are willing to pay more for same-gender and less crowded accommodations, due to concerns about safety and the need for personal space.

Such needs and preferences also shape market practices, as property owners may favor male tenants, assuming they require less space and are less demanding (Harten, 2020). Together, these dynamics create gendered rent differentials, where women incur higher housing costs to access safer and more socially acceptable living environments. While such gendered patterns are documented in informal rental markets, they remain understudied in the increasingly formalized, platform-mediated rental sector. Platform renting differs from traditional rentals by offering standardized listings, automated pricing, and digital, first-come, first-served matching between tenants and units. While this system may reduce direct discrimination based on gender, race, or ethnicity, as observed in the conventional housing market (Bao, 2024; Cao et al., 2025), it also restructures how roommate relationships are formed and housing preferences are translated into market outcomes.

Platforms translate user preferences into algorithmically managed assets, effectively commodifying roommate attributes in shared housing settings (Maalsen and Gurran, 2022; Shrestha et al., 2023). On Ziroom, bedrooms are leased independently, replacing the personalized roommate selection typical of traditional rentals. To maximize occupancy, Ziroom often converts communal areas into additional bedrooms, reducing shared space and limiting social interaction. As a result, platform renting becomes an efficiency-driven, individualized arrangement rather than a socially cohesive living environment.

Although many Ziroom tenants prioritize affordability, location, and room quality over roommate compatibility, gender remains a particularly important factor, particularly for women. Fewer women use Ziroom than men, as those who strictly require same-gender living arrangements opt out. However, among those who do use the platform, preferences for same-gender living environments are likely to persist. Fears of sexual harassment or violence in mixed-gender settings are common concerns (Liu et al., 2014; Xu and Zhang, 2022), while practices such as same-sex dormitories further reinforce gendered expectations. Additionally, traits like cleanliness and good maintenance habits, socially associated with femininity, are often internalized by women as desirable roommate qualities (West and Zimmerman, 1987). These norms may lead women to avoid mixed-gender co-living, whereas men may be more accepting of such arrangements.

Gender composition as a priced amenity

While gendered preferences, driven by safety concerns and social norms, are well documented in traditional and informal rental markets, it remains unclear how they are manifested and translated in platform-mediated settings. In particular, the question is whether, and through what mechanisms, algorithmic leasing systems translate these preferences into observable and potentially amplified market outcomes. Gender may thus become a visible, searchable, and potentially priced amenity, shaping how digital platforms structure and monetize housing options.

This dynamic aligns with hedonic pricing theory, which indicates that price reflects the implicit value of various attributes (Rosen, 1974; Sheppard, 1999). Commodities with more desirable attributes command higher prices. In housing markets, these factors include dwelling characteristics, neighborhood attributes, and accessibility (Benjamin and Sirmans, 1996; Kim, 2016; Lin and Cheng, 2016; Sirmans et al., 1989; Wilson and Frew, 2007). On digital rental platforms, pricing is algorithmically adjusted in response to supply, demand, and user preferences, enabling social attributes such as roommate gender composition to be incorporated into price formation. In this sense, gender composition may function as a priced amenity, similar to more physical housing and neighborhood attributes.

Within this framework, all-female composition is hypothesized to function as a valued amenity in Ziroom’s shared housing market. Given stronger gender homophily among women, their demand for same-gender living arrangements is likely higher than that of men. Therefore, bedrooms with all-female roommates within the apartment are expected to command a premium relative to all-male or mixed-gender units, holding other variables constant.

This framework contributes to the growing scholarship on platform real estate by revealing how platform logics, embedded in user interfaces, data systems, and pricing algorithms, can encode, reflect, and reproduce social disparities (Boeing, 2020; Migozzi, 2024; Wolifson et al., 2024). Extending this line of research from racial or class-based disparities to gender in China, it also builds on Harten’s (2020) analysis of informal bed rental markets.

Compared to informal rental settings studied in Harten (2020), platform-mediated housing introduces several important differences. First, gendered preferences tend to be more explicitly expressed, as tenants are more able to pay for desirable living conditions. While low-cost bed rentals typically cost under 100 USD per bed per month, Ziroom listings consist of two-to-four-bedroom apartments with private rooms averaging around US$200, targeting young, moderate-to-middle-income¹ graduates and professionals. Second, algorithmic pricing replaces individually negotiated rents, enabling systematic analysis of how gender, along with other housing attributes, shapes pricing. Third, unlike informal listings, which often contain fake or duplicate entries (Harten et al., 2021), platform-based data infrastructures provide more consistent and verifiable listings. Such large-scale verified listings enable a clearer disentangling of tenant preferences from discriminatory practices observed in informal markets in shaping rent differences.

Data and method: Gendered price differentials

To empirically test these hypotheses and evaluate whether gender composition functions as a priced amenity in Nanjing’s platform rental market, I draw on a large-scale dataset of bedroom rental listings from Ziroom. Ziroom represents one of the largest and most standardized platform-based rental systems in China, particularly shaping shared housing practices and experiences among tenants. Nanjing is selected as a representative second-tier city with a large inflow of migrants, reflected in the gap between its resident population of 9.58 million and registered population of 7.45 million (Nanjing Municipal People’s Government, 2025), a commonly used proxy for migrant population size in urban China. This context enables an examination of housing decisions under both affordability constraints and emerging platform dynamics.

Using web-scraped platform data, the analysis employs a hedonic pricing model to examine how roommate gender composition, particularly all-female-roommate composition, interact with platform dynamics to explain variations in rent, while controlling for housing, building, and locational attributes. The dataset consists of weekly-collected bedroom listings from the Ziroom website, covering July 2021 to June 2022 (52 weeks). Initially, the web-scraped data comprised 373,964 listings. As listings often remained active for multiple weeks, duplicates were removed by retaining only the most recent observation of each listing. This process results in a final dataset of 42,423 bedroom listings (Figure 1).

Figure 1.

The spatial distribution of Ziroom bedroom listings in Nanjing.

Hedonic price models are used to compare rents across units of different gender compositions. The binary gender² of the current roommates is explicitly observed on the website and was collected. Based on whether the current roommates are all female (ALLWOMEN_i), all male (ALLMEN_i), or gender-mixed (MIXED_i), the gender composition of each listing is classified into one of three categories, which serve as key variables of interest in this study. Unlike individually managed rental businesses, Ziroom does not impose gender-based restrictions, and gender composition evolves over time with tenant turnover.

Each listing includes attributes at multiple levels—bedroom (BEDRM_i), apartment (APT_i), building (BD_i), and residential complex (小区, xiaoqu; COMPLX_i) characteristics—all of which serve as control variables. Bedroom attributes include monthly rent, size, window orientation, presence of an independent bathroom or balcony, and furnishing style. Apartment characteristics include the number of bedrooms and “ting” (厅, denoting living or dining rooms), and whether this room is on the first or top floor, both typically less desirable due to flooding, leakage, or noise.

Building- and complex-level attributes include elevator availability, total number of stories, construction year, and floor area ratio of the complex. Location-related attributes (LOCAT_i) include Euclidean distance from each complex to the nearest metro station, Xinjiekou (新街口), the central business district of Nanjing, and the administrative district. The average transaction price of apartments within each complex is included as a proxy for overall quality, capturing unobserved locational and environmental factors that may influence rents.

In addition to structural and locational factors, platform-specific variables include room status (RMSTATUS_i), which indicates whether the room is available for immediate signing (“sign”), available soon but only reservable (“reserve”), or being subleased (“sublease”). Additional controls (OTHER_i) include monthly management fees and month fixed effects, which capture seasonal variation.

To estimate the effect of gender composition on rents, the following baseline hedonic model is specified, with mixed-gender-roommate units as the reference group:

\begin{array}{l} l n ({R E N T}_{i}) = β_{0} + β_{1} {B E D R M}_{i} \\ + β_{2} {A P T}_{i} + β_{3} {BD}_{i} \\ + β_{4} {COMPLX}_{i} + β_{5} {LOCAT}_{i} \\ + β_{6} {RMSTATUS}_{i} \\ + β_{7} {OTHER}_{i} \\ + α_{1} {ALLWOMEN}_{i} \\ + α_{2} {ALLMEN}_{i} + μ_{i} \end{array}

Results and findings: The role of gender in platform renting

Before presenting the regression estimates, I first examine summary statistics and group-level differences across gender compositions. Table 1 presents descriptive statistics for all variables, disaggregated into all-female, all-male, and mixed-gender units, from which several patterns emerge.

Table 1.

Summary statistics.

Variable	All-female-roommate units (N = 6,882)		All-male-roommate units (N = 13,455)		Mixed-gender-roommate units (N = 22,086)		Total sample (N = 42,423)
Variable	Mean	Std. dev.	Mean	Std. dev.	Mean	Std. dev.	Mean	Std. dev.
Monthly rent (CNY)	1,429	409	1,368	389	1,354	377	1,371	387
Distance to closest metro station (m)	1,347	899	1,453	1,033	1,446	1,021	1,432	1,007
Distance to Xinjiekou CBD (m)	8,890	4,575	10,179	4,644	1,0358	4,345	1,0078	4,506
Districts
Jianye	0.14	0.35	0.12	0.32	0.12	0.33	0.12	0.33
Qixia	0.12	0.33	0.13	0.34	0.14	0.35	0.14	0.34
Jiangning	0.22	0.41	0.31	0.46	0.34	0.47	0.31	0.46
Pukou	0.08	0.28	0.08	0.27	0.07	0.25	0.07	0.26
Xuanwu	0.09	0.29	0.08	0.27	0.07	0.26	0.08	0.27
Qinhuai	0.15	0.36	0.10	0.30	0.09	0.29	0.11	0.31
Yuhuatai	0.07	0.26	0.10	0.30	0.09	0.29	0.09	0.29
Gulou	0.12	0.33	0.08	0.27	0.07	0.26	0.08	0.28
Floor area ratio
0.5–0.8	0.01	0.12	0.02	0.12	0.02	0.13	0.02	0.12
0.8–1.2	0.10	0.30	0.14	0.35	0.11	0.32	0.12	0.32
0–0.5	0.01	0.11	0.01	0.11	0.01	0.11	0.01	0.11
1.2–1.5	0.15	0.35	0.18	0.38	0.16	0.37	0.16	0.37
1.5–2.5	0.36	0.48	0.33	0.47	0.36	0.48	0.35	0.48
2.5–3.0	0.16	0.37	0.13	0.34	0.16	0.37	0.15	0.36
3.0–6.0	0.11	0.32	0.10	0.30	0.11	0.31	0.10	0.31
6.0 above	0.03	0.16	0.02	0.15	0.02	0.14	0.02	0.15
Unknown	0.07	0.25	0.07	0.26	0.05	0.22	0.06	0.24
Average price of the complex (CNY/m²)	33,475	11,673	31,117	10,559	32,484	10,766	32,211	10,885
Construction year
1984–1989	0.00	0.07	0.00	0.07	0.00	0.05	0.00	0.06
1990–1994	0.02	0.15	0.02	0.13	0.01	0.08	0.01	0.11
1995–1999	0.07	0.25	0.05	0.22	0.02	0.15	0.04	0.19
2000–2004	0.11	0.31	0.10	0.30	0.08	0.27	0.09	0.28
2005–2009	0.14	0.34	0.14	0.34	0.16	0.37	0.15	0.36
2010–2014	0.22	0.41	0.24	0.42	0.24	0.43	0.24	0.42
2015–2019	0.39	0.49	0.41	0.49	0.44	0.50	0.42	0.49
Variable	All-women-roommate units (N = 6882)		All-men-roommate units (N = 13,455)		Mixed-gender-roommate units (N = 22,086)		Total sample (N = 42,423)
Variable	Mean	Std. dev.	Mean	Std. dev.	Mean	Std. dev.	Mean	Std. dev.
2020–2022	0.05	0.21	0.04	0.21	0.05	0.21	0.05	0.21
Unknown	0.00	0.06	0.00	0.04	0.01	0.08	0.00	0.07
Building stories
1–3	0.00	0.07	0.00	0.06	0.01	0.08	0.01	0.07
4–6	0.25	0.44	0.29	0.46	0.26	0.44	0.27	0.44
7–9	0.14	0.35	0.12	0.33	0.10	0.30	0.11	0.32
10–17	0.22	0.42	0.21	0.41	0.23	0.42	0.22	0.42
18–25	0.13	0.34	0.13	0.34	0.14	0.34	0.13	0.34
26–33	0.13	0.34	0.13	0.34	0.16	0.36	0.14	0.35
33 or more	0.11	0.31	0.10	0.31	0.11	0.31	0.11	0.31
Unknown	0.00	0.06	0.00	0.05	0.00	0.05	0.00	0.05
Top floor	0.12	0.33	0.12	0.33	0.14	0.34	0.13	0.34
First floor	0.05	0.22	0.07	0.26	0.07	0.25	0.07	0.25
Elevator availability	0.63	0.48	0.61	0.49	0.68	0.47	0.65	0.48
Room area size (m²)	11.71	3.97	11.69	3.92	11.73	4.05	11.72	4.00
ln(room area size)	2.41	0.32	2.41	0.32	2.41	0.32	2.41	0.32
Number of rooms in the apartment
2	0.07	0.25	0.04	0.20	no obs	no obs	0.02	0.15
3	0.30	0.46	0.26	0.44	0.11	0.32	0.19	0.39
4	0.53	0.50	0.57	0.50	0.60	0.49	0.58	0.49
5	0.09	0.28	0.10	0.31	0.22	0.42	0.16	0.37
6	0.02	0.13	0.02	0.15	0.06	0.24	0.04	0.20
7	0.00	0.01	0.00	0.01	0.00	0.04	0.00	0.03
Number of ting
1	0.99	0.09	0.99	0.08	0.99	0.10	0.99	0.09
2	0.01	0.09	0.01	0.08	0.01	0.10	0.01	0.09
3	No obs	No obs	No obs	No obs	0.00	0.01	0.00	0.01
Orientation
East	0.04	0.20	0.03	0.18	0.03	0.18	0.03	0.18
Northeast	0.00	0.04	0.00	0.04	0.00	0.04	0.00	0.04
Southeast	0.00	0.06	0.00	0.06	0.00	0.05	0.00	0.06
North	0.32	0.46	0.30	0.46	0.33	0.47	0.32	0.47
Variable	All-women-roommate units (N = 6882)		All-men-roommate units (N = 13,455)		Mixed-gender-roommate units (N = 22,086)		Total sample (N = 42,423)
Variable	Mean	Std. dev.	Mean	Std. dev.	Mean	Std. dev.	Mean	Std. dev.
South	0.59	0.49	0.62	0.49	0.60	0.49	0.60	0.49
West	0.04	0.20	0.03	0.18	0.03	0.18	0.03	0.18
Northwest	0.00	0.05	0.00	0.04	0.00	0.04	0.00	0.04
Southwest	0.00	0.05	0.00	0.06	0.00	0.06	0.00	0.05
Independent bathroom	0.08	0.27	0.08	0.27	0.10	0.30	0.09	0.29
Independent balcony	0.28	0.45	0.30	0.46	0.28	0.45	0.29	0.45
Furnishing style
NA	0.01	0.10	0.01	0.09	0.01	0.08	0.01	0.09
Buting 4.0	0.16	0.36	0.17	0.37	0.19	0.39	0.18	0.38
Buting 6.0	0.06	0.24	0.06	0.23	0.06	0.23	0.06	0.23
Natie 4.0	0.21	0.41	0.24	0.43	0.21	0.41	0.22	0.41
Natie 6.0	0.06	0.24	0.06	0.23	0.04	0.21	0.05	0.22
Mumian 4.0	0.08	0.27	0.08	0.27	0.09	0.28	0.08	0.28
Mumian 6.0	0.02	0.16	0.02	0.15	0.02	0.14	0.02	0.15
Misu 4.0	0.33	0.47	0.31	0.46	0.33	0.47	0.32	0.47
Misu 6.0	0.06	0.24	0.06	0.24	0.06	0.24	0.06	0.24
Management fees waived	0.42	0.49	0.39	0.49	0.40	0.49	0.40	0.49
Month during which the listing was collected
July 2021	0.04	0.20	0.04	0.20	0.05	0.23	0.05	0.22
August 2021	0.05	0.22	0.05	0.22	0.06	0.24	0.05	0.23
September 2021	0.05	0.21	0.04	0.21	0.05	0.22	0.05	0.21
October 2021	0.07	0.26	0.07	0.26	0.07	0.26	0.07	0.26
November 2021	0.06	0.23	0.06	0.23	0.06	0.23	0.06	0.23
December 2021	0.08	0.27	0.06	0.24	0.06	0.23	0.06	0.24
January 2022	0.04	0.18	0.03	0.18	0.03	0.18	0.03	0.18
February 2022	0.04	0.21	0.03	0.17	0.03	0.17	0.03	0.18
March 2022	0.17	0.38	0.18	0.39	0.17	0.38	0.18	0.38
April 2022	0.07	0.25	0.07	0.25	0.07	0.25	0.07	0.25
May 2022	0.09	0.28	0.08	0.27	0.07	0.26	0.08	0.27
June 2022	0.25	0.43	0.28	0.45	0.27	0.45	0.27	0.45
Room statuses
Reserve	0.05	0.22	0.04	0.20	0.05	0.21	0.05	0.21
Sign	0.80	0.40	0.85	0.36	0.82	0.38	0.83	0.38
Sublease	0.14	0.35	0.11	0.32	0.13	0.34	0.13	0.33

First, rents are highest for all-female-roommate units, averaging 1429 CNY (approximately US$214 in 2022), compared to 1368 CNY for all-male-roommate units and 1354 CNY for mixed-gender roommate units, despite similar average room sizes of around 11.7 m². Second, all-female-roommate units are located in complexes closer to transit and employment hubs. The average distance to the nearest metro station is 1347 m for all-female-roommate units, compared to 1453 m for all-male units and 1446 m for mixed-gender units. Likewise, the average distance to Xinjiekou, Nanjing’s city and employment center, is 8980 m for all-female-roommate units, compared to 10,179 and 10,359 m for all-male and mixed-gender units, respectively. Third, all-female-roommate units are found in less crowded apartments, averaging 3.7 bedrooms, compared to 3.8 bedrooms for all-male units and 4.2 bedrooms for mixed-gender units. Finally, all-female-roommate units exhibit fewer duplications in the raw dataset, an average of 8.0 occurrences, versus 8.9 for all-male and 8.4 for mixed-gender units, indicating shorter market duration and faster turnover.³

These differences between all-female-roommate units and other gender compositions are statistically significant based on t-tests (Table 2). The patterns of higher rents, better locations, and less crowded arrangements for all-female-roommate units are consistent with Harten (2020), which documents similar dynamics in Shanghai’s informal bed rental market. However, two questions remain: to what extent are these rent differences explained by gender composition itself, and how does it interact with platform logics to shape rental outcomes?

Table 2.

T-tests on selected variables.

	All-male & mixed-gender-roommate units	All-female-roommate units	T-test
	Mean value	Mean value	Differences in mean
Monthly rents (CNY)	1359.40	1429.29	−69.89*** (−13.13)
Distance to the closest metro stations (m)	1448.49	1347.29	101.2*** (8.34)
Distance to Xinjiekou (m)	10290.51	8979.84	1310.7*** (21.84)
Number of bedrooms in the apartment	4.07	3.69	0.381*** (36.62)
Duplications in the raw dataset	8.58	7.99	0.589*** (6.69)
Observations (N)	35,541	6882	42,423

T statistics in parentheses. *p < 0.05, **p < 0.01, ***p < 0.001.

Table 3 addresses these questions through hedonic models. Model 1 includes all control variables but excludes gender composition. Model 2 adds gender composition as a key explanatory variable, reflecting the baseline specification. Model 3 further includes interactions between gender composition and room leasing status, allowing gender effects to vary across statuses.

Table 3.

Regression analyses with the logarithm of bedroom listing’s monthly rents (ln_rent) as the dependent variable.

Variable	Model 1	Model 2	Model 3
Variable	b/se	b/se	b/se
Constant (Intercept)	5.933***	5.929***	5.932***
	−0.039	−0.039	−0.039
Location
Ln(Distance to Xinjiekou CBD)	−0.114***	−0.114***	−0.114***
	−0.002	−0.002	−0.002
District fixed effects (ref = Jianye 建邺)
Qixia (栖霞)	−0.127***	−0.127***	−0.127***
	−0.002	−0.002	−0.002
Jiangning (江宁)	−0.074***	−0.074***	−0.074***
	−0.002	−0.002	−0.002
Puko (浦口)	−0.233***	−0.233***	−0.233***
	−0.003	−0.003	−0.003
Xuanwu (玄武)	−0.131***	−0.131***	−0.131***
	−0.002	−0.002	−0.002
Qinhua (秦淮)	−0.112***	−0.112***	−0.112***
	−0.002	−0.002	−0.002
Yuhuatai (雨花台)	0.003	0.004*	0.004*
	−0.002	−0.002	−0.002
Gulou (鼓楼)	−0.120***	−0.120***	−0.120***
	−0.002	−0.002	−0.002
Ln(Distance to closest metro station)	−0.050***	−0.050***	−0.050***
	−0.001	−0.001	−0.001
Building structure and neighborhood quality
Ln(average transactional housing price per sqm of this complex)	0.205***	0.204***	0.204***
	−0.003	−0.003	−0.003
Floor area ratio of the complex (ref = 0–0.5)
0.5–0.8	−0.036***	−0.036***	−0.036***
	−0.005	−0.005	−0.005
0.8–1.2	−0.030***	−0.030***	−0.030***
	−0.004	−0.004	−0.004
1.2–1.5	−0.012***	−0.012***	−0.012***
	−0.004	−0.004	−0.004
1.5–2.5	−0.013***	−0.013***	−0.013***
	−0.004	−0.004	−0.004
2.5–3.0	0.012***	0.012***	0.012***
	−0.004	−0.004	−0.004
3.0–6.0	−0.011***	−0.011***	−0.011***
	−0.004	−0.004	−0.004
6.0 or above	0.016***	0.016***	0.016***
	−0.005	−0.005	−0.005
Unknown/undocumented	0.010**	0.010**	0.010**
	−0.004	−0.004	−0.004
Building construction year (ref = 2000–2004)
1984–1989	0.031***	0.032***	0.032***
	−0.008	−0.008	−0.008
1990–1994	0.016***	0.017***	0.017***
	−0.005	−0.005	−0.005
1995–1999	−0.006**	−0.006**	−0.006**
	−0.003	−0.003	−0.003
2005–2009	0.017***	0.017***	0.017***
	−0.002	−0.002	−0.002
2010–2014	0.000	0.000	0.000
	−0.002	−0.002	−0.002
2015–2019	−0.047***	−0.047***	−0.047***
	−0.002	−0.002	−0.002
2020–2022	−0.075***	−0.075***	−0.075***
	−0.004	−0.004	−0.004
Unknown/undocumented	−0.147***	−0.147***	−0.147***
	−0.007	−0.007	−0.007
Building story (ref = 1–3 story)
4–6	−0.003	−0.003	−0.003
	−0.007	−0.007	−0.007
7–9	−0.023***	−0.022***	−0.022***
	−0.007	−0.007	−0.007
10–17	0.026***	0.026***	0.026***
	−0.008	−0.008	−0.008
18–25	0.008	0.008	0.008
	−0.008	−0.008	−0.008
26–33	0.008	0.008	0.008
	−0.008	−0.008	−0.008
33 or more	0.026***	0.026***	0.026***
	−0.008	−0.008	−0.008
Unknown/undocumented	−0.008	−0.009	−0.008
	−0.012	−0.012	−0.012
Elevator
Elevator is available within the building	0.019***	0.019***	0.019***
	−0.003	−0.003	−0.003
Unit floor within the building (ref = not the first or top floor)
Top floor	−0.031***	−0.031***	−0.031***
	−0.002	−0.002	−0.002
First floor	−0.032***	−0.032***	−0.032***
	−0.002	−0.002	−0.002
Number of bedrooms within this apartment (ref = 2 bedrooms)
3	−0.133***	−0.131***	−0.131***
	−0.005	−0.005	−0.005
4	−0.181***	−0.178***	−0.178***
	−0.005	−0.005	−0.005
5	−0.199***	−0.197***	−0.197***
	−0.005	−0.005	−0.005
6	−0.195***	−0.192***	−0.192***
	−0.006	−0.006	−0.006
7	−0.227***	−0.224***	−0.224***
	−0.02	−0.02	−0.02
Number of “ting (厅)” within this apartment (ref = 1 ting)
2	0.021***	0.020***	0.020***
	−0.008	−0.008	−0.008
3	0.019	0.02	0.02
	−0.049	−0.05	−0.049
Ln(area size of the bedroom)	0.306***	0.306***	0.306***
	−0.003	−0.003	−0.003
Amenities/interior/other
Bedroom orientation (ref = east facing)
Northeast	0	0.001	0.001
	−0.015	−0.015	−0.015
Southeast	0.041***	0.041***	0.041***
	−0.01	−0.01	−0.01
North	−0.025***	−0.025***	−0.025***
	−0.003	−0.003	−0.003
South	0.047***	0.047***	0.047***
	−0.003	−0.003	−0.003
West	−0.003	−0.003	−0.003
	−0.004	−0.004	−0.004
Northwest	−0.059***	−0.059***	−0.060***
	−0.018	−0.018	−0.018
Southwest	−0.01	−0.01	−0.009
	−0.01	−0.01	−0.01
Independent bathroom available within the bedroom	0.313***	0.313***	0.313***
	−0.002	−0.002	−0.002
Independent balcony available within the bedroom	0.096***	0.097***	0.097***
	−0.001	−0.001	−0.001
Furnishing style (ref = unknown)
Buting (布丁) 4.0	0.029***	0.030***	0.030***
	−0.007	−0.007	−0.007
Buting (布丁) 6.0	0.021***	0.021***	0.021***
	−0.007	−0.007	−0.007
Natie (拿铁) 4.0	0.031***	0.032***	0.032***
	−0.007	−0.007	−0.007
Natie (拿铁) 6.0	0.042***	0.042***	0.042***
	−0.007	−0.007	−0.007
Mumian (木棉) 4.0	0.027***	0.027***	0.027***
	−0.007	−0.007	−0.007
Mumian (木棉) 6.0	0.027***	0.028***	0.028***
	−0.008	−0.008	−0.008
Misu (米苏) 4.0	−0.007	−0.006	−0.006
	−0.007	−0.007	−0.007
Misu (米苏) 6.0	0.013*	0.013*	0.013*
	−0.007	−0.007	−0.007
Management fees waived	0.008***	0.008***	0.008***
	−0.001	−0.001	−0.001
Release month fixed effects (ref = July 2021)
August 2021	−0.011***	−0.011***	−0.011***
	−0.003	−0.003	−0.003
September 2021	−0.013***	−0.013***	−0.013***
	−0.003	−0.003	−0.003
October 2021	−0.029***	−0.029***	−0.029***
	−0.003	−0.003	−0.003
November 2021	−0.071***	−0.071***	−0.071***
	−0.003	−0.003	−0.003
December 2021	−0.061***	−0.062***	−0.062***
	−0.003	−0.003	−0.003
January 2022	−0.043***	−0.043***	−0.043***
	−0.004	−0.004	−0.004
February 2022	−0.029***	−0.030***	−0.030***
	−0.004	−0.004	−0.004
March 2022	−0.014***	−0.014***	−0.014***
	−0.003	−0.003	−0.003
April 2022	−0.021***	−0.021***	−0.021***
	−0.003	−0.003	−0.003
May 2022	−0.006**	−0.007**	−0.007**
	−0.003	−0.003	−0.003
June 2022	0.032***	0.032***	0.032***
	−0.003	−0.003	−0.003
Gender composition type of leased bedrooms within the apt (ref = mixed-gender unit)
All-female-roommate unit		0.009***	−0.007
		−0.001	−0.007
All-male-roommate unit		−0.001	−0.001
		−0.001	−0.006
Room leasing status (ref = reserve 可预订)
Sign (可签约)	−0.022***	−0.021***	−0.025***
	−0.002	−0.002	−0.003
Sublease (转租)	−0.017***	−0.017***	−0.021***
	−0.003	−0.003	−0.004
Room condition × Gender type
Sign × All-female-roommate unit			0.018***
			−0.007
Sublease × All-female-roommate unit			0.014*
			−0.008
Sign × All-male-roommate unit			−0.000
			−0.006
Sublease × All-male-roommate unit			0.002
			−0.007
Observations	42,423	42,423	42,423
R-squared	0.863	0.863	0.863
Adj. R-squared	0.863	0.863	0.863

Standard errors are heteroskedasticity robust (HC3).

p < 0.1, **p < 0.05, ***p < 0.01.

Model 1 shows the relationships between bedroom rents and unit-level characteristics, including locational, complex, apartment, bedroom, and platform-specific attributes. The coefficients for “sign” and “sublease” are statistically significant, with values of −0.022 and −0.017, indicating rents 2.2% and 1.7% lower, respectively, compared to rooms under the reference category “reserve.”

These coefficients reflect Ziroom’s dynamic pricing strategy. Rooms are initially listed as “reserve” at the standard rent for several weeks before they become available; if they remain unleased, they are reclassified as “sign” and discounted to encourage immediate move-in. Similarly, “sublease” units, listed by current tenants, tend to be discounted because tenants can set rents below the original rent, and are liable for any rent gaps. These platform-specific rent adjustment mechanisms are captured with precision through hedonic price modeling.

Model 2 introduces two dummy variables for gender composition, all-female and all-male units, with mixed-gender units as the reference category. The coefficient for all-female-roommate units is statistically significant at the 1% level, with a value of 0.009, indicating a 0.9% rent premium relative to mixed-gender units. Given the sample’s average monthly rent of 1370 CNY (around US$205 in 2022), this premium translates to approximately 12.3 CNY. Conversely, the coefficient for all-male-roommate units is not statistically significant, suggesting no rent difference relative to mixed-gender-roommate units.

Model 3 adds interactions between gender composition and room status. After accounting for these interactions, the main effect for all-female units becomes insignificant, while the all-male coefficient remains insignificant. This change suggests that initial rents under the “reserve” status are set similarly across all gender compositions, rather than systematically higher for all-female units by Ziroom’s algorithms. If such bias existed at the initial listing stage, the coefficient for all-female units would have remained significant even after introducing interaction terms.

Additionally, the coefficient for the “sign” category increases in magnitude, from −0.021 in Model 2 to −0.025 in Model 3, suggesting that mixed-gender units under the immediate-move-in status are rented 2.5% less than those under “reserve.” However, the interaction term (sign × all-female-roommate unit) is positive and significant at the 1% level, with a magnitude of 0.018, indicating that all-female units under “sign” exhibit a 1.8% rent premium over comparable mixed-gender units. For the average rent of 1370 CNY, this premium equates to approximately 24.7 CNY per month.

A similar pattern appears for “sublease” units. The coefficient for “sublease” increases in magnitude from −0.017 in Model 2 to −0.021 in Model 3. Additionally, the interaction term between the “sublease” and all-female units (sublease × all-female-roommate unit) is significant at the 10% level with a positive value of 0.014. These results suggest that while, on average, mixed-gender units under the “sublease” status are rented 2.1% less relative to “reserve” units, all-female units under the “sublease” status exhibit a 1.4% premium relative to comparable mixed-gender units.

In contrast, neither the all-male dummy nor its interaction terms with the statuses of “sign” and “sublease” are statistically significantly different from zero, suggesting little rent variations between all-male and mixed-gender units.

Figure 2 summarizes the relative rent levels estimated in Model 3. While all-female units are not priced higher initially, they are discounted less under the “sign” and “sublease” statuses than mixed-gender and all-male units. This pattern suggests stronger demand and faster turnover for all-female units, reducing the need for platform-driven discounts. The observed rent premium cannot be attributed to better location, accessibility, or housing quality, which are controlled for. Instead, it reflects how willingness to pay more for gender-homophilous living arrangements interacts with platform-specific pricing mechanisms to shape rent differences.

Figure 2.

Visualization of model 3 results.

Discussion: Gender, platform logics, and pricing mechanisms

This study leverages unique web-scraped data and hedonic price modeling to examine how gender influences rental prices in platform-mediated shared housing. The findings reveal systematic rent premiums for all-female-roommate units compared to all-male-roommate and mixed-gender-roommate units under specific platform leasing statuses, highlighting how platform mechanisms interact with gendered preferences in shaping rental outcomes. While all comparable bedrooms start with little difference in listing rents on the platform, all-female-roommate units, when available for immediate move-in or sublease, ultimately transact at higher rents. This premium emerges from platform-mediated pricing dynamics, through which algorithmic adjustments translate gendered preferences into monetized attributes. Because women who strictly require same-gender arrangements often opt out of Ziroom, and those who remain tend to prefer them, all-female units become a scarce and valued amenity in the platform’s individualized room-matching system. As a result, women disproportionately bear the cost of these premiums.

Compared to Harten’s (2020) study on informal bed rentals in Shanghai, this study similarly reveals gendered housing outcomes, where women pay for premiums due to gendered needs. In addition, it advances a growing methodological approach that leverages platform-derived data to uncover market dynamics (Harten et al., 2021; Nasreen et al., 2024). Extending this line of research from informal to formal, platform-mediated rental markets, this study shows how platform logic and algorithmic pricing interact with these gendered needs. The observed premium seems to be modest (around 1%–3%), but it is comparable to the impact of building features like elevators or seasonal fluctuations on rents (see Table 3). By focusing on a formal, platform-mediated market, this study offers a more systematic account of how gendered rent disparities emerge within the formalized rental sector in urban China.

Unlike prior research that found male minorities disadvantaged compared with women due to explicit discrimination from property owners (Ahmed and Hammarstedt, 2008; Andersson et al., 2012), this study identifies a subtler mechanism of disadvantage for women in platform-based settings. Here, women are not directly discriminated against by the platform but are indirectly disadvantaged through the pricing of gendered preferences. Although the specific groups experiencing disadvantage differ across contexts, prior studies and the present together study show the gendered nature of rental outcomes. These dynamics are particularly pronounced in social settings where women are more strongly associated with expectations of “feminine virtue,” being tidy, quiet, and easy to live with, while men are often perceived as less compatible with these traits (Bloch, 1987).

The rent premium identified in this study, ranging from 0% to 3%, is comparable to price differentials found in other housing markets. Women in the United States pay about 2% more than men when buying comparable homes (O’Connor et al., 2018), while Black tenants pay comparable levels of rent premiums (Early et al., 2019). Such disparities, while seemingly small, signal broader social inequalities. In China, where women earned only 71.57% of men’s wages in 2015 (Bai et al., 2022), this premium is particularly concerning. Combined with the heightened health risks faced by migrant women (Gideon, 2016), even minor rent increases can exacerbate vulnerability. Thus, the rent premium observed here reflects a concrete manifestation of gender inequality, and may further deepen it.

Adding to recent empirical studies from South Africa, Australia, and the United States, this study of urban China contributes to a growing body of literature on how digital housing platforms restructure access to housing. Prior empirical work has shown how platform-based systems reinforce inequalities, whether through algorithmic tenant sorting, racial and class-based segmentation, or data-driven discriminatory practices (Boeing, 2020; Migozzi, 2024; Wolifson et al., 2024). This study extends those insights by centering gender in the context of China’s platform-based shared housing market. It shows that even in the absence of explicit discrimination, platform mechanisms can amplify gendered disadvantages through pricing. These patterns both reflect and monetize gendered realities, including safety concerns, social expectations around appearance and cleanliness, and internalized norms of femininity (Thébaud et al., 2021; West and Zimmerman, 1987). As such, gender homophily, widely observed in social networks and workplaces, is not merely an individual preference but a socially shaped constraint that becomes financially consequential in platform renting. This case from Nanjing aligns with analyses of how platforms commodify roommate attributes in housing markets (Maalsen and Gurran, 2022; Rogers et al., 2024; Shrestha et al., 2023), and with broader critiques of PropTech’s role in shaping everyday life (Ferreri and Sanyal, 2022; Fields and Rogers, 2021; Nethercote, 2023), highlighting the urgency of incorporating gender-sensitive governance into the design and regulation of housing platforms.

This study has limitations. Ziroom’s primary users are young, single, college-educated migrant professionals with moderate-to-middle incomes. Some tenants with strong preferences for same-gender living or for co-renting with friends may avoid using platforms like Ziroom altogether, instead relying on networks or traditional renting methods. As a result, their stricter preferences for gender homophily are not captured in the dataset, potentially underestimating the rent premium for all-female units among young professionals. Moreover, tenants with lower income or education levels are underrepresented, limiting the generalizability of the findings across broader rental populations. Future research should explore more diverse demographic segments and rental platforms.

Despite these limitations, this study highlights an important mechanism through which gendered inequalities are both reflected and reproduced in platform-mediated housing markets. In a context of persistent gender wage gaps and inequality, the additional cost of securing same-gender co-living reflects not only a financial burden but also a manifestation of broader social constraints. These findings suggest the need for policymakers to address gendered inequalities embedded in algorithmic systems, particularly within China’s rapidly formalizing and platform-mediated rental sector. A gender-sensitive approach to platform governance, such as incorporating gendered safety considerations into housing provision, can help ensure more equitable housing outcomes. Gender, as this study shows, is not only expressed as preference but also monetized through platform pricing, shaping unequal housing opportunities and outcomes.

Footnotes

Acknowledgements

I am grateful to Prof. Jin Xu 徐瑾, Zheng Lin 郑琳, Chen Shiyu 陈诗宇, Wang Haowen 王浩文, and Wang Xinmiao 王鑫淼 at Southeast University for fruitful discussions and field research assistance. I also thank Prof. Nico Heerink, Prof. Gerrit Antonides, and Prof. Eveline van Leeuwen from Wageningen University, and Prof. Sarah Williams from the Massachusetts Institute of Technology, for their constructive feedback and support. Finally, I thank the editor and three anonymous reviewers for their constructive and thoughtful comments.

ORCID iD

Weiyi Cao

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dutch Research Council (Nederlandse Organisatie voor Wetenschappelijk Onderzoek) 482.19.607; National Natural Science Foundation of China 72061137072.

Declaration of conflicting interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Notes

References

Ahmed

Hammarstedt

(2008) Discrimination in the rental housing market: A field experiment on the Internet. Journal of Urban Economics 64(2): 362–372.

Akerlof

Kranton

(2000) Economics and identity. Quarterly Journal of Economics 115(3): 715–753.

Andersson

Jakobsson

Kotsadam

(2012) A field experiment of discrimination in the Norwegian housing market: Gender, class, and ethnicity. Land Economics 88(2): 233–240.

Yang

(2016) Long-term apartment rentals for youth—A new frontier. In: Ba

Yang

(eds) “Internet Plus” Pathways to the Transformation of China’s Property Sector. Springer, pp.145–168.

Bai

Lee

Y-L

Liao

, et al. (2022) The gender pay gap in China: Insights from a discrimination perspective (No. arXiv:2206.09306). arXiv.

Bao

(2024) Is hiding my first name enough? Using behavioural interventions to mitigate racial and gender discrimination in the rental housing market. Urban Studies 61(11): 2156–2175.

Basu

Zuo

Lou

, et al. (2017) Learning to be gendered: Gender socialization in early adolescence among urban poor in Delhi, India, and Shanghai, China. Journal of Adolescent Health 61(4S): S24–S29.

Baum

(2017) PropTech 3.0: The future of real estate. University of Oxford Saïd Business School. Available at: https://www.sbs.ox.ac.uk/sites/default/files/2018-07/PropTech3.0.pdf (accessed 17 May 2026).

Benjamin

Sirmans

(1996) Mass transportation, apartment rent and property values. Journal of Real Estate Research 12(1): 1–8.

10.

Bloch

(1987) The gendered meanings of virtue in revolutionary America. Signs: Journal of Women in Culture and Society 13(1): 37–58.

11.

Boeing

(2020) Online rental housing market representation and the digital reproduction of urban inequality. Environment and Planning A: Economy and Space 52(2): 449–468.

12.

Bokányi

Hannák

(2020) Understanding inequalities in ride-hailing services through simulations. Scientific Reports 10(1): 6500.

13.

Brown

(2023) Prevalence and mechanisms of discrimination: Evidence from the ride-hail and taxi industries. Journal of Planning Education and Research 43(2): 268–280.

14.

Buzar

Ogden

Hall

, et al. (2007) Splintering urban populations: Emergent landscapes of reurbanisation in four European cities. Urban Studies 44(4): 651–677.

15.

Caetano

Maheshri

(2016) Gender homophily and segregation within neighborhoods. Available at: https://research.cgu.edu/dpe-events-and-conferences/wp-content/uploads/sites/37/2016/12/Homophily-Neighborhoods1.pdf (accessed 17 May 2026).

16.

Cao

Heerink

van Leeuwen

(2025) Paying a premium only in native neighborhoods: The role of migrant share in shaping housing price differentials in the Netherlands. Population Space and Place 31(6): e70066.

17.

Charles

Guryan

Pan

(2025) The effects of sexism on American women: The role of norms vs discrimination. Journal of Human Resources 60: 693–742.

18.

Cogné

(2020) ‘Second landlord’ platforms get tenants in debt to fund growth. TechNode. Available at: http://technode.com/2020/06/29/insights-second-landlord-platforms-get-tenants-in-debt-to-fund-growth/ (accessed 17 May 2026).

19.

Early

Carrillo

Olsen

(2019) Racial rent differences in U.S. housing markets: Evidence from the housing voucher program. Journal of Regional Science 59(4): 669–700.

20.

Faxon

Fields

Wainwright

(2024) Beyond the hype: Digital transformations in global land, housing, and property. Environment and Planning D: Society and Space 42(4): 445–455.

21.

Ferreri

Sanyal

(2022) Digital informalisation: Rental housing, platforms, and the management of risk. Housing Studies 37(6): 1035–1053.

22.

Fields

Rogers

(2021) Towards a critical housing studies research agenda on Platform Real Estate. Housing Theory and Society 38(1): 72–94.

23.

Forbes

Collinsworth

Jobe

, et al. (2007) Sexism, hostility toward women, and endorsement of beauty ideals and practices: Are beauty ideals associated with oppressive beliefs? Sex Roles 56(5–6): 265–273.

24.

Gideon

(2016) Migration and health in a Chinese context: Examining the linkages through a gender lens. In: Gideon

(ed.) Handbook on Gender and Health. Edward Elgar Publishing, pp.235–250.

25.

Harten

(2020) Housing single women: Gender in China’s shared rental housing market. Journal of the American Planning Association 87(2): 1–16.

26.

Harten

Boeing

(2024) Access to the exclusive city: Home sharing as an affordable housing strategy. Urban Studies 61(16): 3069–3085.

27.

Harten

Kim

Brazier

(2021) Real and fake data in Shanghai’s informal rental housing market: Groundtruthing data scraped from the internet. Urban Studies 58(9): 1831–1845.

28.

Hess

Acolin

Walter

, et al. (2021) Searching for housing in the digital age: Neighborhood representation on internet rental housing platforms across space, platform, and metropolitan segregation. Environment and Planning A 53(8): 2012–2032.

29.

Hoolachan

McKee

Moore

, et al. (2017) ‘Generation rent’ and the ability to ‘settle down’: Economic and geographical variation in young people’s housing transitions. Journal of Youth Studies 20(1): 63–78.

30.

Jeffers

Esteve

Batyra

(2024) Non-family living arrangements among young adults in the United States. European Journal of Population 40(1): 10.

31.

Kim

(2016) The extreme primacy of location: Beijing’s underground rental housing market. Cities 52: 148–158.

32.

Kleinbaum

Stuart

Tushman

(2011) Discretion within the constraints of opportunity: Gender homophily and structure in a formal organization. Academy of Management Proceedings 2011(1): 1–6.

33.

Laniado

Volkovich

Kappler

, et al. (2016) Gender homophily in online dyadic and triadic relationships. EPJ Data Science 5(1): article 19.

34.

Lin

Cheng

(2016) Access to jobs and apartment rents. Journal of Transport Geography 55: 121–128.

35.

Liu

X-Y

Kwan

Chiu

(2014) Customer sexual harassment and frontline employees’ service performance in China. Human Relations 67(3): 333–356.

36.

Maalsen

(2020) ‘Generation share’: Digitalized geographies of shared housing. Social & Cultural Geography 21(1): 105–113.

37.

Maalsen

Gurran

(2022) Finding home online? The digitalization of share housing and the making of home through absence. Housing Theory and Society 39(4): 401–419.

38.

Maalsen

Rogers

Wolifson

(2024) The social lives of rental proptech: Entanglements between capitalist, care and techno-utopian values. Environment and Planning D: Society and Space 42(4): 476–491.

39.

Maccoby

(1998) The Two Sexes: Growing Up Apart, Coming Together. Harvard University Press.

40.

Migozzi

(2024) The good, the bad and the tenant: Rental platforms renewing racial capitalism in the post-apartheid housing market. Environment and Planning D Society and Space 42(4): 534–558.

41.

MOHURD (2015) The guidelines on accelerating the cultivation and development of rental housing market. Available at: https://www.gov.cn/xinwen/2015-01/14/content_2804042.htm (accessed 17 May 2026).

42.

Nanjing Municipal People’s Government (2025) Population of Nanjing. Available at: https://www.nanjing.gov.cn/zjnj/qhrk/202509/t20250919_5653035.html (accessed 17 May 2026).

43.

Nasreen

Gurran

Shrestha

(2024) Supplementary rental supply? The digital market for low-cost and informal housing in Sydney, Australia. Urban Studies 61(16): 3086–3109.

44.

Net Cultural Industry Research Institute and Ziroom Research Institute (2025) Blue Book on the Development of the Long-Term Rental Market in Chinese Cities 2025 [2025中国城市长租市场发展蓝皮书]. Tsinghua University Real Estate Research Center.

45.

Nethercote

(2023) Platform landlords: Renters, personal data and new digital footholds of urban control. Digital Geography and Society 5: 100060.

46.

O’Connor

Norwood

Kim

, et al. (2018) Gender price differentials in the housing market: Evidence from a repeat-sales approach. SSRN. http://dx.doi.org/10.2139/ssrn.3307563.

47.

Ridgeway

Correll

(2004) Unpacking the gender system: A theoretical perspective on gender beliefs and social relations. Gender & Society 18(4): 510–531.

48.

Rogers

(2016) Uploading real estate: Home as a digital, global commodity. In: Cook

Davison

Crabtree

(eds) Housing and Home Unbound. Routledge, pp.23–38.

49.

Rogers

Maalsen

Wolifson

, et al. (2024) Proptech and the private rental sector: New forms of extraction at the intersection of rental properties and platform rentierisation. Urban Studies 61(14): 2778–2794.

50.

Rosen

(1974) Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy 82(1): 34–55.

51.

Sadowski

(2019) When data is capital: Datafication, accumulation, and extraction. Big Data & Society 6(1): 2053951718820549.

52.

Shaw

(2020) Platform Real Estate: Theory and practice of new urban real estate markets. Urban Geography 41(8): 1037–1064.

53.

Shen

Cai

Wang

, et al. (2021) Changing society, changing lives: Three decades of family change in China. International Journal of Social Welfare 30(4): 453–464.

54.

Sheppard

(1999) Hedonic analysis of housing markets. In: Cheshire

Mills

(eds) Handbook of Regional and Urban Economics, Vol. 3. Elsevier, pp.1595–1635.

55.

Shrestha

Gurran

Nasreen

(2023) From flatmates to realestate? Platform capitalism and the transformation of share housing. Digital Geography and Society 5: 100070.

56.

Sirmans

Benjamin

(1989) Determining apartment rent: The value of amenities, services and external factors. Journal of Real Estate Research 4(2): 33–43.

57.

Steinführer

Haase

(2009) Flexible–inflexible: Socio-demographic, spatial and temporal dimensions of flat sharing in Leipzig (Germany). GeoJournal 74(6): 567–587.

58.

Tang

Guo

Tang

, et al. (2021) Gender-related operational issues arising from on-demand ride-hailing platforms: Safety concerns and system configuration. Production and Operations Management 30(10): 3481–3496.

59.

Thébaud

Kornrich

Ruppanner

(2021) Good housekeeping, great expectations: Gender and housework norms. Sociological Methods & Research 50(3): 1186–1214.

60.

Ullah

Sepasgozar

SME

Wang

(2018) A systematic review of smart real estate technology: drivers of, and barriers to, the use of digital disruptive technologies and online platforms. Sustainability 10(9): 3142. https://doi.org/10.3390/su10093142 Article 9.

61.

Wall

(1989) Leaving home and living alone: An historical perspective. Population Studies 43(3): 369–389.

62.

Wang

(2018) Why China’s home rental market could be the next massive online push for Alibaba, Tencent. Forbes. Available at: https://www.forbes.com/sites/ywang/2018/01/29/why-chinas-home-rental-market-could-be-the-next-massive-online-push-for-alibaba-tencent/ (accessed 17 May 2026).

63.

West

Zimmerman

(1987) Doing gender. Gender & Society 1(2): 125–151.

64.

Wilson

Frew

(2007) Apartment rents and locations in Portland, Oregon: 1992-2002. Journal of Real Estate Research 29(2): 201–218.

65.

Wolf

(2013) The Beauty Myth: How Images of Beauty Are Used Against Women. Random House.

66.

Wolifson

Maalsen

Rogers

(2024) Discrimination in the time of digital real estate: Illustrating a rental schema in the Australian setting. Digital Geography and Society 6: 100088.

67.

(2016) Housing in Chinese urban villages: The dwellers, conditions and tenancy informality. Housing Studies 31(7): 852–870.

68.

Zhang

(2022) Sexual harassment experiences and their consequences for the private lives of Chinese women. Chinese Journal of Sociology 8(3): 421–449.

69.

Wang

(2018) “Three candies” of long-term rental apartment: How Ziroom achieved its brand value. In: Sage Business Cases. Sage Publications, Ltd.https://doi.org/10.4135/9781529763041.

70.

Zhang

Luo

Qiao

, et al. (2023) Financialization, platform economy and urban rental housing: Evidence from Chengdu, China. Applied Geography 156: 102993.

71.

Zhang

Sun

Zhang

(2021) Understanding the role of homeownership in wealth inequality: Evidence from urban China (1995–2018). China Economic Review 69: 101657. https://doi.org/10.1016/j.chieco.2021.101657

72.

Zillow Group, Inc (2023) 2023 Annual report. Available at: https://s24.q4cdn.com/723050407/files/doc_financials/2023/ar/zillow-group-inc-_annual_report_2023.pdf (accessed 17 May 2026).