Financial Services Review, 33(2) 93 Immigration Law Enforcement and Immigrant Homeownership Efthymia Antonoudi,1 Genti Kostandini,2 and HanNa Lim3 Abstract We use the American Community Survey microdata and employ difference-in-differences (DID) models to examine how local immigration law enforcement, through 287(g) agreements and the Secure Communities program, impacts homeownership among different demographic groups. The findings indicate that 287(g) agreements significantly reduce the likelihood of homeownership, particularly among Hispanics without a college education and U.S. citizenship, with effects most pronounced in states lacking E-Verify mandates. The Secure Communities program exhibits more nuanced effects, initially showing positive impacts for specific Hispanic populations; however, these results are not robust to pre-trend analyses. Additional factors such as length of U.S. residence, English proficiency, age, and household income strongly influence immigrant homeownership outcomes, underscoring the complex interplay between policy enforcement and socio-economic assimilation. The results highlight unintended economic consequences of immigration enforcement policies, suggesting important considerations for housing stability, financial security, and integration policies aimed at immigrant and broader community well-being. Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License Recommended Citation Antonoudi, E., Kostandini, G., & Lim, H. (2025). Immigration law enforcement and immigrant homeownership. Financial Services Review, 33(2), 93-123. Introduction According to the 2022 American Community Survey (U.S. Census Bureau, 2024), about fourteen percent of the U.S. population is foreign-born—nearly triple the 1970 percentage. As of 2022, 77% of U.S. immigrants had some form of legal status, such as legally admitted immigrants, refugees, and temporary residents, and nearly half (49%) had become U.S. citizens (Institute of Migration Research, 2019). In addition, as of 2023, about 29.9 million immigrants were employed, a higher workforce participation rate (64.2%) than that of people born in the United States (59.5%) (Bureau of Labor Statistics, 2024). Labor shortages in key industries are often 1 Corresponding author (eanton@uga.edu), University of Georgia, Athens, Georgia, USA. 2 University of Georgia, Athens, Georgia, USA. 3 California State University, Fullerton, Fullerton, California, USA. driven by a lack of skilled workers and an insufficient overall labor supply. Immigrants play a crucial role in addressing these shortages, particularly in physically demanding and specialized sectors where native-born workers are less likely to participate. By supplementing the workforce, immigrants help sustain economic productivity and mitigate the effects of demographic shifts, such as an aging population and declining labor force participation (Sherman et al., 2019). Their contributions are especially significant in industries that face persistent labor gaps, ensuring stability in essential economic sectors. Furthermore, they contribute to business creation, with about a quarter (24%) of all new https://creativecommons.org/licenses/by-nc/4.0/ mailto:eanton@uga.edu https://creativecommons.org/licenses/by-nc/4.0/ Financial Services Review, 34(1) 122 U.S. businesses founded by immigrants (Chodavadia et al., 2024). Immigrants also significantly impact U.S. markets, including the housing market. Not only did immigrants address the housing shortage due to a sharp decline in homebuilding after the 2008 financial crisis by providing 30% of all construction workers nationwide, but they also fueled housing demand with a desire to transition to homeownership (National Immigration Forum, 2024). Homeownership provides significant financial benefits to families as it helps them build wealth and financial security (Goodman & Mayer, 2018). Also, homeownership has been considered an important indicator of assimilation for immigrants, which shows how successfully immigrants integrate into their host country's economic, social, and political life (Sinning, 2010). More importantly, homeownership represents an outcome of the long-term economic progress of immigrant families and a key factor in their long-term financial security (Sinning, 2010). However, there may be significant obstacles for many immigrants in buying a home for reasons specific to them, such as cultural differences in attitudes towards mortgage finance, limited access to loan services, and moving intentions (De Coulon & Wolff, 2010; Rodríguez‐Planas, 2018; Schoenholtz, 2005). There is a significant wealth gap between native and immigrant families (Flores Morales, 2019), so research on homeownership for immigrants is expected to improve immigrant families' financial security, narrowing the existing wealth gap. Researchers have examined how political context affects immigrant assimilation and adaptation to the United States. For example, researchers have investigated how state-level political contexts for immigrants impact educational outcomes for their children (Filindra et al., 2011), how political climates affect immigrant naturalization decisions (Cort, 2012), and how those decisions are associated with increased fears of deportation for unauthorized immigrants (Amuedo-Dorantes et al., 2013). In addition, researchers (Painter & Yu, 2014) examined how the housing bubble 4 E-Verify is an internet-based system that employers use to verify their employees' eligibility to work legally in the U.S. The system compares information an employer enters with records from affected the immigrants' homeownership rates in the United States. However, to our knowledge, no study has considered the impact of immigration policies on homeownership, except for Fu (2017) who examined the effect of E-Verify4 Policies on the homeownership of immigrants. This study attempts to fill this gap by examining the impact of local immigration law enforcement on homeownership. It explores the effects of the 287(g) program and the Secure Communities program on the homeownership of immigrant populations. As part of the Illegal Immigration Reform and Immigrant Responsibility Act of 1996, the 287(g) program allows local police officers to initiate deportation processes for unauthorized immigrants (Amuedo-Dorantes et al., 2018), and the Secure Communities program allows local agencies to run the fingerprints of those arrested through immigration databases to check their immigration status and criminal histories (Miles & Cox, 2014). The 287(g) agreements and Secure Communities programs differ from E-Verify by directly targeting undocumented immigrants instead of employers. Immigration enforcement policies have progressively molded the socio-economic outcomes of immigrant populations in the United States. The 287(g) program and the Secure Communities program are two key policies that have had profound implications for immigrant communities. Both are rooted in local enforcement partnerships with federal immigration authorities, and even though they primarily target undocumented immigrants, they can create ripple effects for broader immigrant populations and U.S.-born citizens (Kohli et al., 2011). Both programs, while aiming at enhancing public safety, have been criticized for their disproportionate impact on immigrants, including increasing deportations and creating a climate of fear that may influence long-term socio-economic decisions such as homeownership (Amuedo-Dorantes et al., 2018; Kohli et al., 2011). the U.S. Department of Homeland Security and the Social Security Administration to confirm employment eligibility. Antonoudi et al. 123 Homeownership is a vital indicator of long- term financial security and economic assimilation for immigrants and can be directly or indirectly affected by these immigration policies. These policies can impact the immigrants' motivation and ability to invest in a home by shifting their local social and political climate (DeWind & Kasinitz, 1997; Sinning, 2010). Researchers have explored the effects of economic, cultural, and individual factors on immigrant homeownership (Amuedo-Dorantes & Mundra, 2013; Borjas, 2002; Chakrabarty et al., 2019). However, little is known about how immigration policies such as the 287(g) and Secure Communities programs precisely affect homeownership outcomes. This study examines how the 287(g) and Secure Communities programs affect homeownership rates among Hispanics, non-citizens, and U.S.- born citizens, exploring whether these policies create financial instability for immigrant households. This study aims to fill this critical gap by examining the relationship between these programs and homeownership, with a focus on spatial (policy environment), exposure (years in the United States), and behavioral (English Proficiency) factors (Xie & Greenman, 2011). This study is important because homeownership is a key pathway to financial security (Goodman & Mayer, 2018) and wealth accumulation, particularly for immigrant and minority communities. Understanding the effects of immigration enforcement on homeownership provides insights into broader financial stability issues and access to credit. The findings have implications for financial institutions, policymakers, and housing market stakeholders by highlighting potential homeownership and financial inclusion barriers. Lenders can use these insights to serve immigrant communities better, while policymakers can assess the economic consequences of immigration policies. This research discusses access to financial services and strategies to support homeownership opportunities. Research Question How do 287(g) agreements and Secure Communities programs impact homeownership probabilities across different population groups? Literature Review Determinants of Homeownership Among Immigrants Immigrant homeownership is often seen as a significant milestone in the economic integration process, signaling long-term stability and assisting in wealth accumulation. Scholars have long studied the individual and household characteristics that drive this outcome. For example, because homeownership rates among immigrant populations in the United States are generally lower than those of native-born Americans, researchers examined the factors that may contribute to this gap, including individual- level characteristics such as national origin (Borjas, 2002), immigration status (Amuedo- Dorantes & Mundra, 2013), and race and ethnicity (Chakrabarty et al., 2019; Mundra & Uwaifo Oyelere, 2018). They also considered community- and national-level external factors such as political climate (Allen & Ishizawa, 2015), economic environments, and housing market conditions (Yu & Myers, 2010). Researchers highlighted that financial risk tolerance, shaped by macroeconomic and demographic factors, is critical in major financial decisions, including homeownership (Kuzniak & Grable, 2017). Additionally, disparities in financial advice-seeking behavior, influenced by income and financial knowledge, may hinder immigrants' access to homeownership resources (Qing & Reiter, 2024). Psychological and demographic variables, such as self-efficacy and financial literacy, further influence immigrants' ability to navigate complex financial decisions like purchasing a home (Kehiaian et al., 2021). Borjas (2002) noted significant variances in immigrant households' homeownership rates across national origin groups. For example, in 1990, Italian immigrants had the highest homeownership rate (78%) in the United States, and immigrants from the Dominican Republic had the lowest (14.2%). Race and ethnicity and birthplace networks (i.e., social networks of immigrants with the same origin) are significant factors in homeownership (Chakrabarty et al., 2019; Mundra & Uwaifo Oyelere, 2018), with some groups (e.g., Chinese, Indian, and Korean natives and immigrants) making headway in homeownership relative to non-Hispanic white Financial Services Review, 34(1) 122 natives, while other groups, such as Black natives, Mexican natives, and Cuban immigrants, seeing their rates decline (Chakrabarty et al., 2019). Using data from the Current Population Survey, Mundra and Oyelere (2018) found that citizenship affected immigrant homeownership more during the recession than before. The authors also found a decreased impact of length of stay in the United States on homeownership probability during 2007-2012 compared to prior years. Similarly, Sinning (2010) showed a significant gap in homeownership rates between natives and immigrants in Germany, and immigrant homeownership rates converge with those of natives over time, contingent on factors like legal status, length of stay, and access to employment and resources. These studies lay the groundwork for understanding the barriers and pathways immigrants face in achieving homeownership. Political and Policy Contexts Affecting Homeownership Much of the literature has overlooked the role of local and federal policy environments, particularly the influence of immigration enforcement measures in shaping homeownership outcomes. Political and economic environments can also be critical in immigrants' homeownership decisions. Amnesty policies providing temporary legal status to eligible undocumented immigrants, according to the Immigration Reform and Control Act (IRCA) of 1986, increased the homeownership rate of eligible immigrants by around four percentage points compared to ineligible immigrants (Sharpe, 2020). Immigrant families in states that adopted E- Verify mandates are less likely to own or buy homes in those states (Fu, 2017), and unfavorable state-level political climates towards immigrants are negatively associated with the probability of homeownership among Asian and Latino immigrants who had moved in the past year (Allen & Ishizawa, 2015). Heightened enforcement deters immigrants, particularly those who may lack legal status, from settling in areas with active policy measures (Amuedo-Dorantes & Mundra, 2013). While their study primarily focuses on mobility and avoidance behaviors, it provides important insights into how enforcement policies disrupt immigrants’ ability to invest in stable, long-term housing. Rugh and Hall (2016) offer a direct examination of the relationship between immigration enforcement and housing stability by examining the effects of 287(g) agreements on Hispanic foreclosure rates, demonstrating how deportation removes wage-earning adults from mixed-status households, increasing the likelihood of foreclosure. Their analysis leverages county-level data and a quasi- experimental approach, revealing that implementing 287(g) agreements led to significantly higher foreclosure rates in affected counties. This research is particularly relevant to the current study as it identifies a precise mechanism – income loss due to deportation – through which immigration enforcement undermines homeownership. Moreover, their findings situate 287(g) agreements as an important policy environment exacerbating racial disparities in housing outcomes. Increased immigration enforcement has been linked to a rise in poverty among households with U.S.-born children, which indirectly affects homeownership by reducing economic resources available for home purchases (Amuedo-Dorantes & Arenas-Arroyo, 2021). Participation in remittance activities negatively impacts homeownership among immigrants, as financial resources are diverted to support families abroad rather than being invested in home purchases (Kuuire et al., 2016). This body of research reveals how immigration enforcement policies intersect with economic and social factors to shape immigrant homeownership outcomes. Prior studies have focused on individual and household-level determinants, but fewer have examined the broader spatial and policy contexts that influence these decisions. This study addresses this gap by investigating how implementing 287(g) agreements and the Secure Communities program affects homeownership among immigrants. Situating this analysis within the current literature contributes to a deeper understanding of the long-term economic impacts of immigration policies on immigrant communities. Immigration enforcement has a significant negative impact on homeownership rates, particularly among Latino communities, by exacerbating financial instability and increasing foreclosure rates (Rugh & Hall, 2016). Immigrant status, legal Antonoudi et al. 123 policies, regional variations, and economic factors are crucial in shaping homeownership trajectories. Conceptual Framework Immigrant research has used different terminologies to measure how well the immigrant population settles into the host country, such as integration, assimilation, and acculturation. There was an effort to distinguish these terminologies from each other. For example, the model of acculturation was defined as "the process of cultural and psychological change that follows intercultural contact" and viewed integration and assimilation as different sectors depending on how people seek to acculturate (Berry et al., 2006). Integration indicates that people adopt the host culture and retain the heritage culture. In contrast, assimilation indicates that people weigh more on involvement with the host society and have less interest in maintaining the heritage culture (Berry et al., 2006). Despite this effort, previous research kept using those terminologies interchangeably. We used "assimilation" in this study but followed the original research's terminologies. The immigrants’ or their children’s educational, health, and financial outcomes can measure the level of the immigrants’ integration, acculturation, or assimilation. Owning a home is an important indicator of an immigrant’s assimilation into the United States, not just because Americans view homeownership as a way to build wealth but also because it indicates an intention to settle in the community and host country. While earlier theories on immigrants' assimilation assumed that assimilation is an integral part of the pathway to the American middle class for immigrants, recent studies recognized the diverse experiences of assimilation and emphasized the importance of the social context (Greenman & Xie, 2008; Xie & Greenman, 2011). Segmented assimilation theory presents heterogeneous assimilation patterns depending on the interactions between immigrants and the host society (DeWind & Kasinitz, 1997). According to the segmented assimilation theory, American society is highly diverse and segmented, and immigrants may take divergent assimilation paths depending on the local social context in which they are embedded (Xie & Greenman, 2011). The factors related to segmented assimilation are categorized into spatial, exposure, and behavioral factors. Spatial factors represent the intensity with which immigrant families are exposed to the host society locally (Xie & Greenman, 2011). Exposure factors represent the length of time spent and exposure in the host society, and behavioral factors explain the individual-level differences in assimilation (Xie & Greenman, 2011). In the present study, the two measures of the spatial factor—county-level 287(g) implementation and Secure Communities implementation—represent how favorable the community’s political environment is toward immigrants. The present study also utilizes years of stay as an exposure factor and English proficiency as a behavioral factor to explain how immigrants are assimilated into the host society—buying a house. These factors are assumed to lower barriers to complicated financial transactions. This study examines these spatial, exposure, and behavioral factors associated with immigrant homeownership. Specifically, it is hypothesized that whether the 287(g) program or/and the Secure Communities program were implemented in the county of residence is associated with the probability of immigrants' homeownership. In addition, it is hypothesized that length of stay in the United States and English proficiency are associated with the probability of immigrants’ homeownership. Immigration Enforcement Policies Immigration enforcement, in the form of police-based measures implemented by local or state police and employment-based measures that establish additional employer requirements (Amuedo-Dorantes & Arenas-Arroyo, 2021), can impact homeownership probabilities. The 287(g) agreements, Secure Communities program participation, and omnibus immigration enforcement agreements are all police-based measures. In contrast, E-Verify is an employment-based measure, and its implementation varies widely by state. The passage of the Illegal Immigration Reform and Immigrant Responsibility Act of 1996 included what is widely known as the 287(g) program. The first 287(g) agreement was signed in 2002, shortly after the 9/11 terrorist attacks. These agreements were implemented based on either the “task force model,” the “jail Financial Services Review, 34(1) 122 model,” or a combination of both, the “hybrid model.” According to a report by the American Immigration Council (2021), the first agreements adopted the jail model, but more task force model agreements were implemented starting in 2006. Under the jail model, police officers could check to see if the person arrested for other law violations has permission to live in the United States and can initiate deportation for those who do not. Under the task force model, officers with appropriate training can interrogate unauthorized immigrants, ask for paperwork, arrest without a warrant, and initiate deportation processes (American Immigration Council, 2021). Because the Obama Administration discontinued the task force model, the hybrid model, the last of the three agreements, expired on December 31, 2012 (Kolker, 2021), and the Department of Homeland Security stopped renewing expired agreements (Pham, 2018). Between 2012 and 2016, only six agreements were renewed— although 287(g) agreements increased from 35 to 150 during the Trump administration from January 2017 to September 2020 (Kolker, 2021). However, these agreements focused on two new models, the "jail enforcement model" and the "warrant service officer model," which differed from the jail and task force models. In this study, we focus our analysis on the 287(g) agreements from 2005-2012. Figure 1 shows the counties that signed 287(g) agreements each year from 2005 until 2012 (Charlton & Kostandini, 2021). Figure 1. Implementation of County 287(g) Policies Over Time as of 2012 (Source: Charlton & Kostandini, 2021) Note: 287(g) counties are in red. Amuedo-Dorantes et al. (2018) found that the 287(g) mandates directly affect immigrant populations through increased deportations and indirectly by increasing the fear of being targeted for removal based on race. Kostandini et al. (2014) noted that county 287(g) mandates reduced the supply of unauthorized immigrant workers and county aggregate agricultural expenditures, farm incomes, and vegetable production. While several studies have found that immigration policies reduce the number of undocumented immigrants in adopting jurisdictions (Bohn et al., 2014; Kostandini et al., 2014; Luo et al., 2018; Watson, 2013), research on their effect on wages and labor participation of undocumented immigrants generally indicates that U.S. immigration laws in the last two decades have not generated the improved labor outcomes for citizen workers that they were intended to deliver. For example, Orrenius and Zavodny (2015) showed that E- Verify mandates reduce average hourly earnings among likely unauthorized male Mexican immigrants but increase labor force participation among likely unauthorized female Mexican immigrants. Amuedo-Dorantes and Bansak (2012) showed that E-Verify mandates reduce the employment likelihood of unauthorized male and female workers but have mixed effects on wages, and a decade later, East et al. (2022) found that the Secure Communities program negatively impacts citizens' employment in middle to high-skill occupations. The implementation of the Secure Communities (SC) program started in March 2008 after 287(g) policies had already been implemented in several counties. The SC program significantly reduced the authority that 287(g) agreements provided to local agencies to perform tasks such as detaining and initiating deportation procedures in place of Immigration and Customs Enforcement (ICE) officers. Under the SC program, police officers can run the fingerprints of those arrested against the Federal Bureau of Investigation (FBI) database and the Department of Homeland Security (DHS) database to check their immigration status and criminal history. If these fingerprint checks reveal that someone is unlawfully present in the United States or otherwise subject to removal, then ICE officers, not local law enforcement agents, take action. Because the SC program was completely activated Antonoudi et al. 123 nationwide by January 2013 (Miles & Cox, 2014), the present study focuses on the years before 2013. Methodology Data and Sample We use microdata from the American Community Survey (ACS) by the United States Census Bureau from the IPUMS database. ACS surveys households and produces nationally representative information on the population’s social, economic, housing, and demographic characteristics yearly (U.S. Census Bureau, 2021). Specifically, we use the 2005-2013 ACS data and focus on the head of household. We examine the effect of county-level 287(g) agreements and the Secure Communities (SC) program on the homeownership of all Hispanics, Hispanics without a college education and U.S. Citizenship (hereinafter "HWCC"), and U.S.-born citizens. We focus on these three groups for two reasons: (1) even though HWCC immigrants are directly affected, all Hispanics may also be affected since police checks may target them; (2) U.S.- born citizens living in jurisdictions with 287(g) agreements may benefit from HWCC workers leaving these jurisdictions, which may make it easier for them to find a house to purchase and become homeowners. Because ACS does not provide information on whether an immigrant is undocumented, we rely on previous literature to focus on the immigrant population—Hispanics without a college education and U.S. Citizenship (HWCC) - who are at higher risk of lacking legal status and most impacted by immigrant law enforcement. We use two ways to identify those immigrants from the ACS data that are commonly used in the literature (Amuedo- Dorantes & Arenas-Arroyo, 2021; Amuedo‐ Dorantes & Bansak, 2014; Bohn et al., 2014; Orrenius & Zavodny, 2015; Passel & Cohn, 2010). The first, Hispanics from Mexico without a college education and U.S. 5 A total of 289 PUMAs contained counties that passed 287(g) legislation at some point during the period of the analysis and the rest (1,812 PUMAs) had no counties with 287(g) legislation. From the 289 PUMAs that had 287(g) legislation, 271 entirely consisted of counties (or part of counties) that passed 287(g) legislation, and 18 PUMAs contained a mix of counties that passed 287(g) legislation and citizenship (hereinafter "HWCC1"), include individuals who emigrated from Mexico with a high school diploma or less and are not naturalized U.S. citizens. Previous literature (Orrenius & Zavodny, 2015; Passel & Cohn, 2010) focused on this group because, although not all immigrants in this group have undocumented immigration status, a high proportion belongs to this category. The second definition for immigrants used in separate models as a robustness check includes those who are non-citizens, of Hispanic origin (which includes immigrants from El Salvador, Guatemala, Honduras, and other Latin American countries), and have a high school diploma or less (hereinafter "HWCC2"). We should note that ACS did not provide county-level information before 2005. Beginning that year, it includes county information if the county’s population exceeds 65,000 and provides Public Use Micro Data Areas (PUMAs) for each respondent. PUMAs are areas with a minimum of 100,000 residents and do not cross state lines. Some counties contain several PUMAs, and some PUMAs are made of several small counties. However, the 287(g) and SC programs are implemented at the county level. As a result, it is difficult to determine whether individuals in PUMAs, including those in several counties, are subject to the immigration policy. Following Kostandini et al. (2014), we exclude PUMAs from multiple counties with at least one program county.5 Empirical Approach We use difference-in-differences (DID) models to identify associations between the variables selected over time and compare the differential effect of immigration law enforcement in the jurisdictions that implemented the 287(g) agreements with those that did not. The DID approach is well-suited for this study because comparing changes in homeownership rates before and after policy implementation between treatment and control groups isolates the effect counties that did not pass 287(g) legislation. The 18 PUMAs that contained a mix of counties were dropped from the primary analysis. However, we ran robustness checks that included all 289 PUMAs containing at least one (or part of one) 287(g) county, and the main results did not change. The results are available from the authors upon request. Financial Services Review, 34(1) 122 of 287(g) agreements and Secure Communities programs from broader economic trends that may influence homeownership. This method is commonly used in policy evaluation and is particularly effective in settings where a policy is implemented in some locations but not others, allowing for a natural comparison. Additionally, including time and location fixed effects helps control for baseline differences between areas and broader macroeconomic conditions that could affect housing markets. We estimate the effect of the agreements on immigrant homeownership by comparing the average change over time in the outcome variable for the treatment group and the average change over time for the control group. Given that the dependent variable (homeownership) is binary (1 = homeowner, 0 = non-homeowner), we estimate the following logit model to assess the effect of 287(g) and Secure Communities programs: Pr(Yi,p,t = 1) = F(β0 + β1IMMIGPOLp,t + β2Xi,t + β3Zp,t + γp + ηt + εi,p,t) This specification uses a logistic transformation, which is more appropriate than OLS regression when the outcome is a dummy variable. In addition to the raw logit coefficients, we report marginal effects, allowing for a more straightforward interpretation of how immigration enforcement policies affect the probability of homeownership. We identify the effects of county-level immigration policies (i.e., 287(g) agreements and the SC program) on the probability of homeownership. Let Yi,p,t be the outcome of interest on the household head i in PUMA p and year t. We regress the dependent variable, which is a dummy variable equal to one if the household head owns a house and zero otherwise, on an indicator variable IMMIGPOLc,t equal to one if the head of the household is located in PUMA p that had a 287(g) policy in year t. We then run the same model for the SC programs where the IMMIGPOLc,t is equal to one if the individual is located in a PUMA with an SC program in year t. We control for a vector of individual and family characteristics Xi,t, which includes exposure and behavioral factors from the segmented assimilation theory and control variables. Following previous studies, we used the length of stay in the United States (in years) as an exposure factor and English proficiency (does not speak English, poor English, good English, very good English) as a behavioral factor. We control for age, number of children, level of education (a bachelor’s degree or not), and household income (logged). We also control for a vector of time-variant PUMA characteristics Zp,t, which includes indicator variables for location in an E-Verify state and 287(g) state. We control for PUMA fixed effects γc and year fixed effects ηt. Ei,p,t is the error term. While 287(g) agreements and Secure Communities programs were implemented as separate immigration enforcement policies, there was some overlap in their adoption. Some counties implemented only one of the two programs, while others adopted both. Secure Communities was introduced in March 2008 and expanded nationwide, eventually covering all jurisdictions by 2013, whereas 287(g) agreements at the county level started in 2005 and were implemented selectively based on agreements between local and federal authorities. To account for this overlap, we control for both policies' presence, ensuring that each enforcement measure's estimated effects are not conflated. We include an indicator variable for Secure Communities when estimating the effects of 287(g) agreements and vice versa, allowing us to isolate the independent effect of each policy on homeownership outcomes. This approach ensures that the estimates reflect the distinct impact of each program while accounting for jurisdictions that may have implemented both policies. Other researchers have used 287(g) agreements (e.g., Charlton and Kostandini, 2021; Kostandini et al., 2014) and the SC program (e.g., Miles and Cox, 2014) in DID frameworks and provided additional discussions on policy exogeneity and other robustness checks. Results Table 1 presents the summary statistics of our sample from 2005-2013 ACS data on homeownership and other characteristics included in the empirical model. The homeownership rate in the total sample was 67.1%. About 85% of the sample were U.S.- born, 9% were Hispanics, and 2% were HWCC1. The homeownership rate of each group was 69.1%, 51.5%, and 39.1%, Antonoudi et al. 123 respectively. On average, the study's sample was about 43 years old, had less than one child, and had a household income of about $77,000. About a third of the sample (33.9%) had at least a bachelor's degree. Among those who were not born in the United States, their average years in the U.S. was about 20, and the majority speak English well (22.3%) or very well (34.8%). Table 1. Summary Statistics of Main variables Mean Std. Dev. N U.S.-born 0.848 0.359 6,841,478 Hispanics 0.087 0.282 6,841,478 HWCC1 0.023 0.151 6,841,478 Homeownership 0.671 0.470 6,841,478 Homeownership among the U.S. born 0.691 0.462 5,803,208 Homeownership among Hispanics 0.515 0.500 593,781 Homeownership among HWCC1 0.391 0.488 160,251 Age 42.959 10.416 6,841,478 Number of children 0.193 0.502 6,841,478 Household income 77,067.374 77,097.301 6,841,478 Bachelor’s degree or higher 0.339 0.473 6,841,478 Post 287(g) county 0.079 0.269 6,841,478 Post-Secure Communities County 0.178 0.383 6,841,478 Post-E-Verify state 0.038 0.190 6,841,478 Years in the U.S. among the non-U.S.-born 20.464 12.716 1,038,270 No English among the non-U.S.-born 0.056 0.229 1,038,270 Poor English among the non-U.S.-born 0.165 0.371 1,038,270 Good English among the non-U.S.-born 0.223 0.416 1,038,270 Very good English among the non-U.S.-born 0.348 0.476 1,038,270 Speaks only English non-U.S.-born 0.209 0.407 1,038,270 Note: Data are from the 2005-2013 American Community Survey. Age is provided in years, household income is in U.S. dollars, and all other variables are dummy variables. Model 1: The Effect of 287(g) Agreements on Homeownership We start by examining homeownership using only residents of states with 287(g) agreements as controls (columns 1–3) and residents in states without E-Verify (columns 4–6). These results are presented in Table 2, which shows the estimates of the DID model in equation (1). As mentioned, the impact of 287(g) agreements on homeownership is examined using logit models within a difference-in-differences framework as specified in equation (1). Results in Table 2 and the respective marginal effects presented in Table 3 indicate a statistically significant negative relationship between 287(g) agreements and homeownership rates for Hispanics, HWCC1 (Hispanics without college education and U.S. citizenship), and U.S.-born citizens. The estimated effect of 287(g) agreements on homeownership probability for Hispanics is a decline of 5.6 percentage points. For HWCC1, the effect is even more significant, with a reduction of 7.4 percentage points, suggesting that these agreements disproportionately affect immigrants with lower educational attainment. The most considerable observed effect is among U.S.-born citizens, where the estimated decline in homeownership probability is 7.9 percentage points. Financial Services Review, 34(1) 122 Table 2. Logit Regression Results (The Effect of 287(g) Agreements on Home Ownership Using Only Residents of States with 287(g) Agreements at the County and State Level (columns 1, 2, and 3) as Controls and Residents in States Without E-Verify (columns 4, 5 and 6)) Hisp HWCC1 U.S.-Born HispE HWCC1 E U.S.-BornE (1) (2) (3) (4) (5) (6) Post 287g county -.287*** -.386*** -.447*** -.183*** -.416*** -.556*** (0.067) (0.085) (0.052) (0.067) (0.084) (0.051) Years in the United States .033*** (0.001) .048*** (0.002) .028*** (0.001) .051*** (0.002) Poor English .223*** (0.003) .106*** (0.004) .231*** (0.037) 0.111*** (0.037) Good English .531*** (0.043) .338*** (0.044) .531*** (0.043) .354*** (0.044) Very Good English .42*** (0.044) .271*** (0.049) .37*** (0.045) .285*** (0.048) Only English .171*** .049 .211*** .062 (0.057) (0.069) (0.058) (0.067) Age .056*** .043*** .071*** .054*** .042*** .071*** (0.0008) (0.001) (0.00004) (0.0008) (0.001) (0.0004) Number of Children .194*** .148*** .355*** .214*** .146*** .398*** (0.011) (0.015) (0.009) (0.01) (0.015) (0.008) Bachelor’s degree or higher .272*** (0.021) .278*** (0.016) .218*** (0.02) .184*** (0.019) Log of Household Income .894*** .618*** .964*** .931*** .622*** 1.012*** (0.018) (0.028) (0.008) (0.018) (0.027) (0.01) U.S.-born .762*** (0.032) .668*** (0.035) Obs. 412,660 115,833 2,784,784 453,081 121,335 4,348,766 Note: Data are from the 2005-2013 American Community Survey (ACS). Logistic regressions apply individual weights provided by the ACS and include year-fixed effects. Robust standard errors are clustered at the PUMA level. The outcomes variable is a dummy variable indicating homeownership. Logistic regressions control for SC as well as E-Verify and state level 287(g) agreements in columns (1-3) and state level 287(g) agreements in columns 4-6. *, **, *** denote significance levels at the 10, 5, and 1 percent levels, respectively. The results remain consistent when focusing on individuals in states without E-Verify as controls. The probability of homeownership for Hispanics in these states declines by 3.6 percentage points, while the effect for HWCC1 in these states is a decrease of 7.9 percentage points. For U.S.-born citizens in states without E-Verify, the probability of homeownership decreases by 9.5 percentage points. Antonoudi et al. 123 Table 3. Marginal Effects of 287(g) Agreements on Home Ownership Group dy/dx Std. Err. z p-value 95% Confidence Interval Hispanic -.0562 .0130 -4.32 < .001 [-0.0817, -0.0307] HWCC1 -.0735 .0159 -4.61 < .001 [-0.1047, -0.0423] U.S.-Born -.0785 .0091 -8.64 < .001 [-0.0964, -0.0607] Hispanics E -.0359 .0132 -2.72 .007 [-0.0617, -0.0100] HWCC1 E -.0793 .0159 -4.99 < .001 [-0.1105, -0.0481] U.S.-Born E -.0952 .0087 -10.92 < .001 [-0.1123, -0.0781] Note. dy/dx represents the marginal effects after the implementation of 287(g) agreements. Standard errors are based on the delta method. p-values are reported to three decimal places, with < .001 indicating high statistical significance. Confidence intervals are reported at the 95% level. A leads-and-lags model tests the assumption of parallel trends, with results presented in Figure 2. The reference period is the year of adoption, which is set to 0 in the figure. The findings confirm that homeownership trends were similar between treatment and control groups before implementing 287(g) agreements for Hispanics and HWCCI. After policy implementation, the adverse effects on homeownership persist and align with the main estimates for these two groups. However, the pre-trends do not support the findings in Table 2 (columns 3 and 6), suggesting that 287(g) agreements are associated with a decline in homeownership among U.S.-born citizens and immigrant populations because of the presence of pre-trends. Thus, the results for U.S.-born citizens are invalidated by the leads-and-lags model. Beyond the direct effects of 287(g) agreements, several exposure and behavioral factors significantly influence homeownership probabilities. More extended residence in the United States is positively associated with homeownership, suggesting that time allows immigrants to accumulate financial resources and establish stability in housing markets. English proficiency also plays a critical role, with individuals who speak English well being more likely to own a home, likely due to improved access to financial services and better employment opportunities. Demographic and economic factors further reinforce these trends. Age and household income are positively related to homeownership across all groups, reflecting the life-cycle accumulation of assets and the financial capacity needed for home purchases. The number of children is also associated with a greater likelihood of homeownership, potentially due to families seeking stable housing. Within the Hispanic sample, U.S.-born individuals exhibit higher homeownership rates than foreign-born Hispanics, highlighting advantages such as unrestricted access to mortgage markets and greater financial literacy. These findings suggest that while 287(g) agreements negatively impact homeownership, broader structural factors continue to shape housing outcomes, with financial stability and assimilation playing key roles in mitigating policy effects. Model 2: The Effect of Secure Communities on Homeownership In the second model, we examine the rollout of the Secure Communities program in the United States, and, as noted earlier, we take advantage of the variation in the timing of adoption among different counties to examine, using the same DID model, whether secure communities have affected homeownership in adopting jurisdictions. The logistic regression model examines the impact of the Secure Communities program on homeownership. As previously noted, this program was implemented gradually across different counties, allowing for a difference-in- differences approach to identify its effects. Table 4 presents estimates using two different control groups: residents of states without county-level 287(g) agreements (columns 1-3) and residents of states without both E-Verify and county-level 287(g) agreements (columns 4-6). Financial Services Review, 34(1) 122 Table 4. The effect of Secure Communities on Home Ownership Using Only Residents of States Without County Level 287(g) Agreements (columns 1, 2, and 3) as Controls and Residents in States Without E-Verify and County Level 287(g) Agreements as Controls (column 4, 5 and 6) Hisp HWCC1 U.S.-Born HispE HWCC1 E U.S.-BornE (1) (2) (3) (4) (5) (6) Post SC County -.028 .051 .042 .321*** .305** .061 (0.058) (0.085) (0.029) (0.085) (0.015) (0.048) Years in the United States .017*** (0.002) .057*** (0.003) .015*** (0.002) .058*** (0.003) Poor English .294*** (0.053) .149*** (0.057) .03*** (0.002) .156*** (0.061) Good English .591*** (0.056) .438*** (0.062) .581*** (0.061) .426*** (0.068) Very Good English .356*** .356*** .358*** .369*** (0.06) (0.076) (0.065) (0.082) Only English .516*** 0.1 .532*** .172 (0.078) (0.122) (0.086) (0.131) Age .05*** .04*** .071*** .049*** .041*** .07*** (0.001) (0.003) (0.0006) (0.002) (0.003) (0.0006) Number of Children .27*** .144*** .42*** .271*** .153*** .424*** (0.016) (0.027) (0.011) (0.018) (0.028) (0.012) Bachelor’s degree or higher .104*** (0.033) .081*** (0.029) .097*** (0.036) .062*** (0.032) Log of Income .987*** .583*** 1.039*** .993*** .582*** 1.045*** (0.027) (0.038) (0.016) (0.03) (0.041) (0.018) U.S.-born .329*** (0.069) .283*** (0.075) Obs. 118,336 27,895 2,617,485 102,970 24,086 2,322,113 Note: Data are from the 2005-2013 American Community Survey (ACS). Logistic regressions apply individual weights provided by the ACS and include year-fixed effects. Robust standard errors are clustered at the PUMA level. The outcomes variable is a dummy variable indicating homeownership. Logistic regressions control for state-level 287(g) agreements and E-Verify (columns 1-3) and state-level 287(g) agreements (columns 4-6). *, **, *** denote significance levels at the 10, 5, and 1 percent levels, respectively. Table 5. Marginal Effects of Secure Communities Program on Home Ownership Group dy/dx Std. Err. Z p-value 95% Confidence Interval Hispanic E .0621 .0164 3.80 < .001 [0.0301, 0.0942] HWCC1 E .0579 .0226 2.56 .010 [0.0136, 0.1022] Note. dy/dx represents the marginal effects after the implementation of the Secure Communities program. Standard errors are based on the delta method. p-values are reported to three decimal places, with < .001 indicating high statistical significance. Confidence intervals are reported at the 95% level. Results in Table 4 indicate moderate but significant positive effects of Secure Communities on homeownership among Hispanics (column 4) and HWCC1 (column 5), and the rest of the coefficients are not significant at conventional levels. Table 5 presents the marginal effects of the significant coefficients. More specifically, the probability of homeownership for Hispanics increased by 6.2 percentage points. The probability of homeownership for HWCC1 increased by 5.8 percentage points in jurisdictions with SC after the implementation compared to those in the control group; however, as illustrated in Figure Antonoudi et al. 123 3, which shows the pre-trend analysis, the parallel pre-trends assumption does not hold for any of the two groups, thus invalidating the results for these groups. Robustness Checks To ensure the validity and reliability of our main findings, we conducted a series of robustness checks using alternative specifications, broader control groups, and expanded definitions for immigrant populations. The results from the robustness checks reinforce the primary conclusions drawn from our analysis. First, we tested the sensitivity of our findings to alternative control groups. Since DID results depend on the control group, we provide additional analysis using a larger pool in the control group to examine the effect of 287(g) agreements and the SC program. The results for 287(g) agreements are presented in Table 6. While they contain the same outcomes and independent variables as those in Table 2, the control group in the first three columns includes all U.S. residents. In addition, the specification in column (4) focuses only on U.S.-born Hispanics; the specification in column (5) focuses on HWCC1 living in the United States for more than 10 years, and the last specification (column 6) focuses only on white U.S.-born citizens. These results, displayed in Tables 6 and 7 for the 287(g) program and Tables 8 and 9 for the Secure Communities program, remain consistent with the main estimates. For instance, the marginal effects presented in Table 7 indicate significant negative impacts of 287(g) agreements on homeownership probabilities. More precisely, the implementation of 287(g) agreements leads to a decline in the probability of homeownership of approximately 3.4 percentage points for Hispanics (p<0.001), 6.9 percentage points for HWCC1 (p<0.001), and 9.1 percentage points for U.S.-born citizens (p<0.001). These marginal effects are very close in magnitude to our primary results, providing further evidence that 287(g) policies adversely affect homeownership, especially for HWCC1 and U.S.-born residents in policy-affected areas. Financial Services Review, 34(1) 122 Table 6. The Effect of 287(g) Agreements on Home Ownership Using All Residents of the United States as Controls Hisp HWCC1 U.S.-Born HispUS HWCC1 10yrs U.S.-Born-W (1) (2) (3) (4) (5) (6) Post 287g County -.174*** -.361*** -.529*** -.169*** -.370*** -.552*** (0.063) (0.081) (0.05) (0.058) (0.082) (0.053) Years in the United States .029*** (0.001) .051*** (0.001) Poor English .227*** (0.003) .114*** (0.033) .056*** (0.037) Good English .524*** (0.039) .357*** (0.04) .228*** (0.042) Very Good English .378*** (0.041) .288*** (0.044) .294*** (0.044) Only English .221*** .07 .189*** (0.053) (0.062) (0.066) Age .054*** .043*** .012∗∗∗ .063*** .056*** .073*** (0.0007) (0.001) (0.00002) (0.0009) (0.001) (0.0004) Number of Children .213*** .151*** .059∗∗∗ .237*** .057*** .44*** (0.009) (0.013) (0.0005) (0.013) (0.016) (0.008) Bachelor’s degree or higher .222*** (0.018) .072∗∗∗ (0.0005) .252*** (0.021) .097*** (0.017) Log of Income .919*** .606*** .167∗∗∗ 1.019*** .679*** .952*** (0.016) (0.024) (0.0003) (0.017) (0.025) (0.009) U.S.-born .674*** (0.032) Obs. 516,994 140,590 5,123,700 237,950 98,411 4,308,027 Note: Data are from the 2005-2013 American Community Survey (ACS). Logistic regressions apply individual weights provided by the ACS and include year-fixed effects. Robust standard errors are clustered at the PUMA level. The outcomes variable is a dummy variable indicating homeownership. Logistic regressions control for SC, state-level 287(g) agreements, and E-Verify. *, **, *** denote significance levels at the 10, 5, and 1 percent levels, respectively. Table 7. Marginal Effects of 287(g) Agreements on Home Ownership using all residents of the United States as Controls Group dy/dx Std. Err. z p-value 95% Confidence Interval Hispanic -.0342 .0124 -2.75 < .001 [-0.0585, -0.0098] HWCC1 -.0688 .0153 -4.49 < .001 [-0.0989, -0.0388] U.S.-Born -.0911 .0085 -10.67 < .001 [-0.1078, -0.0744] Hispanic E -.0327 .0111 -2.94 .003 [-0.0546, -0.0109] HWCC1 E -.0807 .0177 -4.56 < .001 [-0.1153, -0.0460] U.S.-Born E -.0857 .0086 -9.95 < .001 [-0.1026, -0.0688] Note. dy/dx represents the marginal effects after the implementation of 287(g) agreements. Standard errors are based on the delta method. p-values are reported to three decimal places, with < .001 indicating high statistical significance. Confidence intervals are reported at the 95% level. As presented in Tables 8 and 9, the Secure Communities program shows significant, though more minor, positive marginal effects on homeownership probability for Hispanics (2.8 percentage points, p<0.001) and HWCC1 (3.1 percentage points, p=0.006), consistent Antonoudi et al. 123 with our earlier findings. However, interpretation requires caution due to some parallel trend violations highlighted above and in the Appendix Figures A.1 and A.2. Table 8. The effect of SC on Home Ownership Using all Residents of the United States as Controls Hisp HWCC1 U.S.-Born HispUS HWCC1 10rys U.S.-Born-W (1) (2) (3) (4) (5) (6) Post SC County .142*** .16*** -.044*** .127*** -.142** -.036* (0.037) (0.058) (0.018) (0.037) (0.06) (0.019) Years in the United States .028*** (0.001) .049*** (0.001) Poor English .227*** (0.032) .111*** (0.032) .061* (0.035) Good English .517*** (0.037) .35*** (0.038) .291*** (0.04) Very Good English .376*** (0.039) .29*** (0.041) .308*** (0.041) Only English .213*** .063 .187*** (0.049) (0.058) (0.062) Age .055*** .043*** .071*** .063*** .056*** .073*** (0.0007) (0.001) (0.0003) (0.0008) (0.001) (0.0004) Number of Children .208*** .141*** .384*** .233*** .053*** .438*** (0.009) (0.012) (0.007) (0.012) (0.015) (0.008) Bachelor’s degree or higher .221*** (0.017) .192*** (0.015) .253*** (0.02) .101*** (0.017) Log of Income .909*** .6*** .987*** 1.007*** .667*** .94*** (0.014) (0.022) (0.008) (0.016) (0.024) (0.009) U.S.-born .672*** (0.03) Obs. 585,549 157,949 5,741,823 271,970 112,722 4,823,266 Note: Data are from the 2005-2013 American Community Survey (ACS). Logistic regressions apply individual weights provided by the ACS and include year-fixed effects. Robust standard errors are clustered at the PUMA level. The outcomes variable is a dummy variable indicating homeownership. Logistic regressions control county-level 287(g) agreements, state-level 287(g) agreements, and E-Verify. *, **, *** denote significance levels at the 10, 5, and 1 percent levels, respectively. Financial Services Review, 34(1) 122 Table 9. Marginal Effects of Secure Communities on Home Ownership using all Residents of the United States as Controls Group dy/dx Std. Err. z p-value 95% Confidence Interval Hispanic .0280 .0073 3.82 < .001 [0.0136, 0.0423] HWCC2 .0305 .0112 2.74 .006 [0.0087, 0.0524] U.S.-Born .0076 .0032 2.40 .017 [0.0014, 0.0137] Hispanic E .0245 .0072 3.41 .001 [0.0104, 0.0386] HWCC2 E .0308 .0130 2.37 .018 [0.0053, 0.0562] U.S.-Born E .0059 .0031 1.94 .052 [0.0001, 0.0120] Note. dy/dx represents the marginal effects after the implementation of 287(g) agreements. Standard errors are based on the delta method. p-values are reported to three decimal places, with < .001 indicating high statistical significance. Confidence intervals are reported at the 95% level. Another potential concern is whether the observed effects of immigration enforcement policies are confounded by broader housing market trends, particularly in the aftermath of the 2007–2009 Great Recession. To account for this, we introduce a Bartik-style index constructed at the state level. The results are similar to the main findings.6 Finally, as the validity of our difference-in-differences estimations critically depends on the parallel trends assumption, we conducted dynamic analyses (leads-and-lags models) to explore pre-existing trends, as shown in Figures 2 and 3 and Figures A.1 and A.2 in the Appendix for the robustness analysis using the expanded control groups. For the 287(g) agreements, the parallel trends assumption holds well for Hispanics and HWCC1, supporting the potential causal interpretation of our main results. However, significant pre-existing trends among U.S.-born citizens suggest that caution is needed when interpreting the magnitude of effects for this group. For the Secure Communities program, the assumption of parallel trends is not supported, particularly for Hispanics and HWCC1, suggesting caution in interpreting these results as causal. These robustness checks, complemented by marginal effects analysis, substantiate our main 6 We follow Watson (2013) and Yasenov (2019) and include in the DID model a macroeconomic control variable for the impact of the Great Recession, namely the Bartik-style measure, which could be a proxy for the labor demand shocks and the trend of the unemployment rate over the Great Recession period. The Bartik-style measure is constructed as follows: Bartik= 𝐵𝑎𝑟𝑡𝑖𝑘 = ∑ 𝑠ℎ𝑎𝑟𝑒𝑞𝑟 2000 𝑞 × ∆2000𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑞𝑡 where 𝑠ℎ𝑎𝑟𝑒𝑞𝑟 2000 is the conclusion: immigration enforcement policies, particularly 287(g) agreements, exert significant negative impacts on homeownership probabilities across the various demographic groups examined. These findings persist even when subjected to alternative definitions of immigrant status, expanded control groups, and additional housing market controls, underscoring their broader economic significance. 7 Pre-trends The most critical assumption of DID is the parallel pre-trend assumption that control and treatment groups should exhibit similar trends before policy implementation. For this reason, we employ a dynamic model (with full leads and lags relative to the pre-adoption year) to examine whether pre-existing differential trends may partly explain significant differences between the treatment and control groups after policy implementation. If that is the case, we cannot attribute our findings to policy implementation since they might be due to pre-existing differences and not immigration laws. The dynamic analysis results are presented in Figure 2 and Figure 3 for each significant finding concerning the specifications in Table 2 industry share in state r in the year 2000. ∆2000𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡𝑞𝑡is the nationwide growth of industry q between 2000 and year 𝑡. Industry q includes construction, agriculture, finance, government, information, manufacturing, professional, retail trade, wholesale trade, and transportation. Results are very similar to the main findings and are available upon request. 7 All these results are available upon request. Antonoudi et al. 123 for 287(g) agreements and specification in Table 4 for the SC program, and the reference time period is the year of adoption, which is set to zero across all specifications. Each panel in the figure plots the estimates of the dynamic DID model and the 95 percent confidence interval. The results in Figure 2, which assess the pre-trends for 287(g) agreements, suggest that the parallel trends assumption holds for Hispanics and HWCC1. However, there is some evidence of a significant downward trend in homeownership among U.S.-born citizens before the implementation of 287(g) that invalidates the post-treatment results. Similarly Figure 3, which presents pre-trends for Secure Communities, shows apparent pre-existing differences across groups that do not support the positive significant findings for Hispanics (column 4 of Table 4) and HWCC1 (column 5 of Table 4). Figure 2. Pre-trends on the Effects of 287(g) Agreements Among the Different Groups -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post Hispanics -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post HWCC1 Financial Services Review, 34(1) 122 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post U.S.-Born -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post Hispanics E -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post HWCC1 E Antonoudi et al. 123 Figure 3. Pre-trends on the Effects of Secure Communities Among the Different Groups with Statistically Significant Results Across all robustness checks, our main conclusions remain unchanged. The pre-trends for the additional models used in robustness checks are provided in Figures A.1 and A.2 in -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post U.S._Born E -1 -0.5 0 0.5 1 1.5 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post Hispanics -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post HWCC1 Financial Services Review, 34(1) 122 the Appendix. The adverse effects of 287(g) agreements on homeownership are pronounced among Hispanics and HWCC1. Secure Communities programs do not appear to affect homeownership. The robustness of these findings across alternative control groups, broader immigrant definitions, and housing market controls strengthens the credibility of our results. It suggests that strong immigration enforcement policies like 287(g) agreements have significant lasting effects on homeownership decisions. Discussion The findings of this study indicate that implementing 287(g) agreements is associated with statistically significant changes in homeownership rates across multiple demographic groups. While HWCC1 experience the most pronounced declines in homeownership Hispanic subpopulations also exhibit significant changes. These results suggest that immigration enforcement policies have broader housing market implications beyond the intended policy targets, affecting both immigrant and non-immigrant populations. The impact of 287(g) agreements is particularly pronounced among HWCC1 with marginal effects indicating a reduction of approximately 7.5 percentage points. The decline was also prominent for Hispanics, where the probability of homeownership decreased by a magnitude of 3.6 to 5.6 percentage points. This effect was consistent across different model specifications and different alternative definitions of HWCC. These findings align with prior research indicating that immigration enforcement policies can disrupt economic stability and reduce long-term investments such as homeownership (East et al., 2022; Rugh & Hall, 2016). The significant negative effects for Hispanics further suggest that immigration enforcement policies create broader economic spillovers, possibly by reducing local economic activity, discouraging home purchases, or increasing housing market uncertainty. The economic effects of Secure Communities, on the other hand, are different compared to 287(g) agreements and they do not indicate smaller changes in homeownership across all groups, but the pre-trends analysis does not support these findings. This is not surprising as 287(g) agreements were a lot more aggressive compared to Secure Communities. The study also reinforces the role of exposure and behavioral factors in shaping homeownership outcomes. Consistent with prior literature, more extended residence in the United States is positively associated with homeownership, highlighting the importance of financial accumulation and market integration over time (Chatterjee & Zahirovic-Herbert, 2014; Kim et al., 2012; Mundra & Uwaifo Oyelere, 2018). English proficiency remains a key determinant of homeownership, as those who speak English well are more likely to secure mortgage loans and navigate real estate transactions. The relationship between age, income, and homeownership is consistent with existing research, as financial stability and family size influence the decision to invest in long-term housing (Goodman & Mayer, 2018). The differential effects observed across groups suggest that while economic stability plays a crucial role in homeownership, immigration enforcement policies introduce additional barriers that disproportionately impact specific populations. Overall, the results suggest that 287(g) agreements have considerable negative effects on homeownership with the most significant declines observed for HWCC1 and Hispanics, particularly in states without E-Verify. These findings highlight how immigration enforcement interacts with economic and demographic factors to shape homeownership trends. Implications Immigration policy has been associated with notable declines in homeownership among HWCC1 and Hispanics. This trend suggests immigration enforcement policies may have broader economic implications beyond their intended targets. Homeownership serves as a fundamental component of economic stability and wealth accumulation. Therefore, reductions in homeownership rates can lead to decreased community investment and hinder economic growth. These findings align with research indicating that intensified immigration enforcement can reduce economic activity and consumer spending, adversely affecting local economies. Antonoudi et al. 123 Moreover, industries heavily relying on immigrant labor, such as construction and service sectors, may experience workforce shortages due to restrictive immigration enforcement. Such shortages can increase labor costs and delay housing projects, exacerbating existing housing shortages and affordability issues. These dynamics underscore the need for balanced immigration policies considering labor market demands and the potential economic consequences of a reduced workforce. Beyond economic factors, immigration enforcement policies also have social implications. The fear and uncertainty generated by these policies can lead to decreased civic participation and trust in public institutions among immigrant and non- immigrant populations. The observed declines in homeownership among HWCC1 and Hispanics suggest that these policies may create financial instability for a broader segment of the population than initially intended. This erosion of financial security could have long-term consequences, reinforcing disparities in wealth accumulation and economic mobility. Policymakers should consider comprehensive immigration reform that provides transparent and fair pathways to legal status for immigrants lacking legal immigration status, thereby reducing the negative impacts of enforcement- focused approaches on housing markets and local economies. Implementing initiatives that facilitate the integration of immigrants into the labor force, particularly in sectors experiencing labor shortages, can support economic growth and stability. Additionally, investing in programs that promote financial literacy and homeownership support for immigrant and minority populations may help mitigate some of the economic disruptions caused by immigration enforcement policies. By adopting a holistic approach that balances enforcement with integration and support, policymakers can mitigate the adverse effects of immigration policies on homeownership and broader economic indicators, fostering more resilient communities. Limitations While this study provides valuable insights into the impact of immigration enforcement policies on homeownership rates, several limitations should be acknowledged. First, the reliance on available data sources may not fully capture the complexities of individual legal statuses, as such information is often underreported or misclassified. Second, the study focuses on 287(g) agreements and Secure Communities. However, other immigration enforcement measures at the federal, state, and local levels may also contribute to changes in homeownership rates, which are not fully captured in this analysis. Variations in local enforcement intensity and community cooperation with federal authorities could result in heterogeneous impacts that our analysis might not fully address. Third, while our difference-in-differences approach attempts to control for unobserved confounders, there remains the possibility of omitted variable bias. Factors such as local economic conditions, housing market dynamics, and social networks could also influence homeownership decisions but are challenging to measure comprehensively. Fourth, the cross-sectional nature of the data limits our ability to establish causal relationships definitively. Lastly, the generalizability of our findings may be constrained by regional differences in policy implementation and demographic compositions. Future research should consider exploring these variations to enhance the external validity of the results.Addressing some of these limitations in subsequent studies would contribute to a more nuanced understanding of how immigration enforcement policies affect housing outcomes among diverse populations. Conclusion The findings of this study provide important insights into how immigration enforcement policies impact homeownership among different demographic groups. The results indicate that 287(g) agreements strongly negatively affect homeownership, particularly for HWCC1 and Hispanics. These findings underscore the broader economic and social consequences of immigration enforcement, which extend beyond the intended targets of these policies. The results suggest that such policies contribute to financial instability, discourage long-term investments, and reshape local housing markets. 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Pre-trends on the Effects of the 287(g) Agreements Among the Different Groups with Statistically Significant Results for the Model Using all Residents of the United States as Controls Presented Under Tables 6 and 7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post Hispanics -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post HWCC1 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post U.S.-Born Antonoudi et al. 123 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post Hispanics Born in the U.S. -0.6 -0.4 -0.2 0 0.2 0.4 0.6 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post HWCC1 10 or less years in the U.S. -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post U.S.-Born, White Financial Services Review, 34(1) 122 Figure A.2. Pre-trends on the effects of the Secure Communities Program Among the Different Groups with Statistically Significant Results for the Model Using all Residents of the United States as Controls Presented Under Tables 8 and 9 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post Hispanics -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post HWCC1 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post U.S.-Born Antonoudi et al. 123 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post Hispanics Born in the U.S. -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post HWCC1 10 or less years in the U.S. -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 4 years prior 3 years prior 2 years prior 1 year prior Adoption 1 year post 2 years post 3 years post 4 years post U.S.-Born White