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Article

A Preliminary Analysis of the Relationships Between Rising Temperatures and Residential Rental Rates in the USA

by
Michael A. Garvey
1,* and
Tony G. Reames
2
1
Office of Management, Energy, and Impact, U.S. Department of Energy, Washington, DC 20585, USA
2
School for Environment & Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(16), 7459; https://doi.org/10.3390/su17167459
Submission received: 12 July 2025 / Revised: 13 August 2025 / Accepted: 15 August 2025 / Published: 18 August 2025
(This article belongs to the Section Sustainable Urban and Rural Development)

Abstract

Climate change poses significant challenges to the economic and social sustainability of urban dwellers, particularly in the real estate market, where rising temperatures are affecting property values. While most research focuses on how climate change impacts buyers and sellers, this study shifts attention to renters, who may be more vulnerable to climate-induced price increases. By analyzing rental price and climate data, this study uses ordinary least squares (OLS) and fixed-effects regressions to assess the impact of temperature fluctuations on rental rates across 50 major U.S. metropolitan areas. The findings reveal a positive and significant relationship between rising temperatures and rental rates, particularly in the Northeastern and Southern U.S. These results suggest that targeted policy interventions may help ease financial pressures on vulnerable renters and support more sustainable urban development over time. The analysis also highlights the potential role of energy efficiency measures in rental housing to lower energy costs and alleviate rent burdens. Additionally, the findings indicate that local policymakers may consider rent stabilization strategies and investments in urban green infrastructure to protect low-income renters, reduce localized heat exposure, and promote long-term urban resilience.

1. Introduction

Global warming is a significant threat, with heat as the leading weather-related cause of human mortality [1]. Factors like CO2 levels and urban heat island effects contribute to rising metropolitan temperatures, posing a risk to both social wellbeing and economic stability [2]. The housing market is particularly vulnerable to climate change, facing threats such as rising sea levels, more frequent hurricanes, wildfires, and shorter winters. Approximately 2% of U.S. homes—worth a combined USD 882 billion—are at risk of being inundated by rising sea levels by 2100 [3]. Homes exposed to sea level rise sell for approximately 7% less than observably equivalent unexposed properties equidistant from the beach [4]. Housing is a central factor in creating sustainable communities worldwide [5]. Sustainable communities aim to protect the environment, address social needs, and promote economic success. To address the climate crisis, governments must implement plans that ensure cities are prepared—environmentally, economically, and socially—to adapt to and mitigate future climate impacts.
By 2080, temperatures in U.S. metropolitan cities are expected to be similar to cities currently hundreds of miles to the south and southwest [6]. However, rising temperatures do not impact all households and neighborhoods equally. In major U.S. cities, high-density housing contributes to higher temperatures, creating disparities between suburban and urban areas [7]. The current literature shows that differences in homeowner risk beliefs can inflate property values and exacerbate market instability in climate-exposed areas [8]. As heat levels continue to naturally rise, household temperature inequality continues to increase. Suburban homes are typically located in heavily shaded areas, surrounded by parks and vegetation. Temperatures around these homes are significantly cooler on the same day and time than temperatures around homes in neighboring yet much denser and less shaded urban areas. This density divide can translate into renter/owner and associated racial differences [9]. Evidence shows that minorities are significantly less likely to own their homes than Whites. Blacks and Hispanics are much less likely to own than Asians and face multiple barriers to homeownership such as segregation [10].
Previous research indicates that heat influences the market for single family home purchases. For example, Sussman et al. [11] found that the prices of homes in large and small cities does increase as ambient temperatures increase, all else being equal. Less is known about the impact of increasing ambient temperature on rental markets. Renters, primarily minorities and low-income households, are particularly affected due to their concentration in heat islands—urban areas significantly hotter than their rural surroundings. These heat islands result from extensive concrete and asphalt, which absorb and retain heat, exacerbating the effects of global warming on urban residents [12]. This study examines how global warming affects rental rates in densely populated metropolitan areas, focusing on the economic effects of rising heat on housing.
Extreme heat can affect the habitability of rental units, particularly in older buildings in need of weatherization and/or without modern cooling systems [13]. A substantial amount of energy is consumed to heat and cool houses. In the U.S., residential buildings account for 22% of primary energy consumption, of which space conditioning (i.e., heating and cooling) accounts for 41% [14]. Aside from increasing financial burden, this can also lead to health issues, particularly among vulnerable populations such as the elderly and those with pre-existing health conditions. Similar tensions have also been documented in global studies, where inadequate tenant protections during periods of urban regeneration and environmental stress have exacerbated displacement risks and weakened social cohesion [15].
Housing markets are seasonal. In many national and regional markets, prices and transaction volumes move in a predictable pattern over a year [16]. Seasonal differences in temperature between summer and winter have a statistically significant, positive association with seasonal differences in GDP [17]. Climate change-related damage affects the economic structure of various regions [18]. Recent studies predict that climate change will lead to a redistribution of the population across the United States as people choose to locate to regions less susceptible to extreme climate [19]. Understanding these dynamics is crucial for developing socio-economic policies that protect renters from the adverse effects of climate change. This means developing housing models that amenable not only to resilience and sustainability against climate change, but also to the comfort, health, and well-being of occupants, as highlighted in recent housing frameworks studies [20].
We hypothesize that hotter temperatures will have a negative effect on metropolitan rental rates, particularly in already heat-stressed areas, driving down costs as demand is shifted toward cooler localities. As temperatures continue to increase, we also expect summer months to show a greater share of reduced rates, while the inverse should be true for winter months.
This research addresses three main questions: First, we explore the relationship between rising temperatures and metropolitan rental rates. Second, we explore the role of seasonality in rental rate increases. Lastly, we identify which regions are most vulnerable to future shifts in climate patterns.
This study aims to fill the gap in understanding the impact of climate change on rental markets. By focusing on renters, who often have less financial stability and fewer resources to adapt to changing conditions, we can better understand the broader social and economic impacts of climate change. This contributes to the growing body of literature on the economic impacts of climate change and is intended to provide insights for policymakers, urban planners, and housing advocates.

2. Materials and Methods

Using rental price data from Zillow and weather data from the National Oceanic Atmospheric Association (NOAA), this study analyzes the effects of heat on metropolitan housing rental markets. The Zillow Observed Rent Index (ZORI) database was used for information pertaining to residential rental units in metropolitan localities. While temperature values were drawn from the National Oceanic and Atmospheric Administration’s National Centers for Environmental Information Climate at a Glance database. Together, these values aid in conducting analysis on the effects of climate change on rental rates. This study’s data collection strategy parallels those of Baldauf et al. (2020) and Bernstein et al. (2019), who also combined NOAA forecasts with Zillow geocoded home transaction data [3,4]

2.1. Housing Price Data

Housing data from Zillow includes changes in the asking rents over time and are currently calculated for the 100 largest metropolitan areas in the United States. In this study, we explore 50. The data cover 9 years of monthly price observations, from 2014 to 2021. The data supplied are smoothed, 3-month-weighted, moving average measures of the typical observed market rate rents across a given subset of the population.
Currently, ZORI data are available for both U.S. ZIP code level and metropolitan level. This study utilizes the metropolitan level data since climate disparities are not disaggregated at a more granular level.
Typically, the supply of units available to rent at any given time can change quite frequently, and average or median price measures across time may not reflect actual market-based movements in rent prices. In fact, these measures simply reflect the fact that certain unit types are available at different times. The ZORI database is specifically selected due to its ability to calculate same-unit price differences over time and aggregate the differences across all rentals that are listed repeatedly on Zillow. Data from the ZORI database reflects a whole-market perspective with a weighted index associated with the U.S. Census Bureau. The weights assigned to the most frequently listed rental units are smaller values than those listed less frequently in the Zillow Database relative to the census [21].
Structural data concerning year built, number of units, and building age for listed units go back to 1940 and are embedded into the ZORI database via the American Community Survey. Units are listed as single-unit, 2–4 units, and 5+ units.
Regressing the differences between home rent prices on the time change between sales provides coefficients for month-over-month changes in the index values.

2.2. Climate/Temperature Data

The climate values used to capture rising temperatures are from the National Oceanic Atmospheric Administration’s National Centers for Environmental Information Climate at a Glance database. The range of data available covers 1895 to the present and provides average monthly air temperature values. The available monthly weather data are at global, national, regional, state, county, and metropolitan levels [22]. This study reviews monthly temperature averages at the regional, state, and metropolitan levels to attempt to control for the true effect of climate change effects on residential rental rates.
By reviewing the NOAA data on the same seasonal basis as the Zillow rent rates, this research seeks to identify the effect of increased heat levels on the rental housing market. This approach closely follows that of Sussman et al. [11], with the main difference being the analysis of rental properties as opposed to homeowners.

2.3. Fixed-Effects Model Development

In this study, we employ a series of fixed-effects regression equations to parse out the true effects of rising temperatures on residential rental rates. Three of these are fixed-effects models that control for the geographic heterogeneity of weather patterns across the country. We progressively build up the following models.

2.3.1. Model 1: Temperature Fixed Effects

The first model looks at the effects of national average temperatures on residential rental rates alone. This is done to analyze the initial relationship between the variables by checking for statistical significance and for the direction of the relationship. Nationally, there are isolated microclimates across the different geographic regions, states, and metropolitan statistical areas and the results of Model 1 do not control for this heterogeneity. To conduct this initial regression pass, we merged the metropolitan-level Zillow rental rate data with the metropolitan-level NOAA climate data to assess the explanatory power of the predictor variable of interest: average monthly temperatures’ effect on residential rental rates.
To complement the regression analysis, Appendix A provides summary tables and visualizations showing rental rates and average temperatures for a representative sample of metropolitan areas across each U.S. region. These charts help illustrate the observed variation in climate exposure and rental pricing patterns that motivate the modeling approach.

2.3.2. Model 2: Seasonal Fixed Effects

The second model uses a similar set-up as the first OLS regression but introduces a season dummy as a fixed instrumental variable. Seasonal effects capture the differing rental price trends that occur on an annual basis. Data are grouped by corresponding year and month. We create a seasonal time trend by grouping the months associated with each season together under a unique season variable.
As such, December, January, and February values are grouped together into a winter season. March, April, and May values are grouped together into a spring season. June, July, and August values are grouped together into a summer season. Lastly September, October, and November values are grouped together into a single fall season.
The data is then able to be reviewed monthly, annually, and seasonally. By choosing to expand to a fixed regression model, we can control for and better observe the relationship between any periodic time signals within the data and their unique climate scenario. The model, in its current state, is unable to make the case for causality.

2.3.3. Model 3: Year and Month Fixed Effects

It is well-known that there are seasonal weather patterns that occur on an annual basis. Temperatures shift on a national level and, regardless of location, there are noticeable changes in weather arrays. Model 2 does not account for regular time trend fluctuations in temperature. To the extent that, on an annual basis, we capture rising heat levels, there are also heterogenic shifts in the lengths and magnitudes of seasons on a national level. In some years and months, we see that summer and winter seasons are becoming longer and stronger in magnitude in comparison to shorter springs and falls. In Model 3, the YEAR/MONTH time dummy captures that variation in annual temperature patterns. This is to test if and how rental rates differ when considering seasonal time trends.

2.3.4. Model 4: Regional Fixed Effects

Despite seasons, there are different segments of the country that have their own historical and traditional set of weather patterns. To account for the historical climate trends embedded within different regions throughout the United States, we include regional fixed effects. The four geographic regions selected—Northeast, South, Midwest, and West—are in accordance with the U.S. Census Bureau [23].
In addition to the regions, included are state and metropolitan fixed effects to capture microclimate-level distinctions within localities. The West and South are known to be warm regions with relatively short winters and long summers. The Northeast is typically prone to longer and harsher winter months than other locations. In Model 4, because there are known climate differences among geographic sections, we include regional fixed-effects variables to capture such differences.

2.3.5. Model 5: State Fixed Effects

The fifth model controls for, not just the region, but the heterogeneity of states located within regions. Adding this additional instrumental variable helps better analyze the effects of a locality’s weather on its respective rental market rates. This model is useful for many state-level generalizations and can aid in the production of effective environmental justice policy.
r e n t i t = β 1 + γ 1 t e m p e r a t u r e i + γ 2 s p r i n g i + γ 3 s u m m e r i + γ 4 f a l l i + γ 5 n o r t h e a s t i + γ 6 s o u t h i + γ 7 w e s t i + d u m m i e s   f o r   m o n t h y e a r t + d u m m i e s   f o r   s t a t e i + ε i t

2.3.6. Model 6: Metropolitan Fixed Effects

There are factors within states outside of rising temperatures that can lead to the variation of metropolitan rental rates. In Model 6, as allowed by the data, we rerun the regression from Model 5 but interchange the state fixed-effects variable with a city-level fixed-effects variable to test for correlations between metropolitan rental rates and rising temperatures. Here, the variable of interest returns a negative and statistically significant value with decreased explanatory power.
r e n t i t = β 1 + γ 1 t e m p e r a t u r e i + γ 2 s p r i n g i + γ 3 s u m m e r i + γ 4 f a l l i + γ 5 n o r t h e a s t i + γ 6 s o u t h i + γ 7 w e s t i + d u m m i e s   f o r   m o n t h y e a r t + d u m m i e s   f o r   c i t y i + ε i t
The results reflect that fact when controlling for metropolitan level fixed effects, increased temperatures during fall months in all regions compared to the Midwest result in reduced rental rates. Model 6 has the greatest adjusted R-squared value (0.9758), which implies that, when controlling for city-level fixed effects, more of the variation associated with residential rental rates is explained by the average monthly temperatures. The methodological process, from data inputs through the six regression models, is summarized in Figure 1.
This framework summarizes the sequential model specifications (Models 1–6), beginning with temperature-only OLS regressions and progressively adding seasonal, year/month, regional, state, and metropolitan fixed effects. Data sources include the Zillow Observed Rent Index (ZORI) and the NOAA Climate at a Glance database.

3. Results

The regression analysis, seen below in Table 1, reflects the effects of climate change on residential rental rates given the included controls. The findings indicate a statistically significant relationship between temperature increases and rental price fluctuations, with notable variations across different model specifications. The inclusion of fixed effects helps to refine these relationships, highlighting how seasonal, regional, and city-level dynamics mediate the impact of temperature changes on rental prices. The following sections detail the influence of each model (1–6) and analyze the impact of adding successive controls.

3.1. Model 1: Base Model

The initial model estimates a positive relationship between average monthly temperature and rental prices. Without controlling for seasonal or regional factors, we find that for each degree in temperature increase, rental rates increase by $2.22. This model provides a broad overview of the relationship but lacks distinction of explaining variations across and within different contexts. The value of this model lies in its simplicity and ability to capture an overall trend. However, its main weakness is the inability to account for other influences that are likely to confound the temperature effect, leaving open potential omitted variable biases.

3.2. Model 2: Seasonal Fixed Effects

Model 2 introduces seasonal fixed effects to account for predictable variations in rental prices throughout the year. The inclusion of spring, summer, and fall dummies help isolate the effect of temperature from typical seasonal fluctuations in the housing market, relative to the winter season. The results indicate that temperature maintains a positive relationship with rental prices. However, relative to winter rental prices, summer and fall have a negative relationship with rental prices. Adding seasonal controls improves the explanatory power of the model by accounting for seasonal rental price shifts but does not yet address regional differences in pricing trends. At present, these results suggest that seasonality plays a key role in shaping rental market trends.

3.3. Model 3: Year and Month Fixed Effects

Building on Model 2, Model 3 incorporates both year and month fixed effects, which account for macroeconomic time trends and cyclical variations in rental pricing, such as inflation, policy changes, and economic cycles. The inclusion of these controls reduces the potential for unobserved time-related biases, while helping to isolate the impact of temperature fluctuations more precisely. Though temperature remains a significant positive driver of rental prices, the coefficients for seasonal effects become slightly weaker, suggesting that part of the previously observed variation was due to broader economic conditions rather than localized temperature effects. Relative to the winter season, fall is positively associated with rental prices. The benefit of these controls is that they filter out time-specific influences that would otherwise confound the results.

3.4. Model 4: Regional Fixed Effects

Model 4 incorporates regional fixed effects, which control for structural differences in rental pricing across geographic areas. The inclusion of Northeast, South, and West regional independent variables, with the Midwest as the baseline group, refines the analysis by capturing differences in housing demand, climate resilience, and economic conditions across regions. The results show that temperature maintains a positive relationship with rental prices. Relative to the Midwest, rental prices in the Northeast, South, and West regions are higher. The addition of regional controls enhances the model’s accuracy by addressing geographic disparities but does not yet account for state or city-specific dynamics that may further influence rental prices.

3.5. Model 5: State-Level Fixed Effects

Model 5 introduces state-level fixed effects, controlling for differences in statewide policies, economic conditions, and housing regulations that may influence rental prices. By accounting for these broader contextual factors, the model isolates the within-state effect of temperature on rents. The results show that while temperature remains a statistically significant predictor, its effect size weakens compared to earlier models—indicating that part of the previously observed relationship was confounded by state-level dynamics. This suggests that statewide interventions, such as climate adaptation policies or housing regulations, may buffer some of the rental price impacts of rising temperatures.

3.6. Model 6: Metropolitan-Level Fixed Effects

In this final model, state fixed effects are replaced with metropolitan fixed effects, increasing spatial granularity but slightly reducing explanatory power (R-squared = 0.976). This shift exposes variation within major cities, revealing a negative and statistically significant relationship between rental rates and temperature. Previously masked intercity differences now emerge, accounting for local economic and housing factors that influence rent prices.
Seasonal fixed effects remain positive, though the effect of spring on rent prices is positive and marginally significant (p < 0.10), both summer and fall seasons reflect strong statistical significance at the p < 0.01 level. We also see fall rents increase by USD 297.31. Regional fixed effects show strong, positive relationships in the Northeast and West, aligning with their historically high rental markets, while the Midwest remains less affected.
This trend is also reflected visually in Appendix A, where Figure A2 and Table A2 show consistent heat–rent correlations in Southern cities such as Dallas and Miami. Conversely, Appendix A’s Figure A4 and Table A4 show relatively smaller rental rate fluctuations in Midwestern cities such as Chicago, St. Louis, and Des Moines, consistent with their role as the baseline group and with lower exposure to extreme temperature shifts.
These results suggest that cooler regions may become more desirable as temperatures rise, ultimately increasing rents, while areas already hot may see rent stabilization or decline due to declining livability. This challenges the initial hypothesis of a universal positive relationship between temperature and rent, instead pointing to localized economic and policy-driven variations. Further research should explore how climate-driven rental shifts disproportionately impact certain populations, particularly in cities with limited housing supply and weaker climate adaptation policies.
Overall findings (Shown in Table 1) indicate a dynamic yet statistically significant relationship between increasing national temperatures and increasing metropolitan rental rates. The findings reflect that, compared to the Midwest, the Western region is the least sensitive to increased pricing due to rising temperatures, while the Northeastern and Southern regions are the most sensitive.

3.7. Synopsis of Results

The findings from Model 6 underscore the spatial heterogeneity in the relationship between temperature and rental prices. The negative and statistically significant temperature coefficient suggests that in some metropolitan areas—particularly those already experiencing extreme heat—higher temperatures are associated with declining rental rates. This may reflect diminished livability, tenant migration, or other climate-driven market adjustments, though further research is needed to isolate the exact mechanisms.
The strong seasonal effects, particularly in fall and summer, may reflect delayed heat impacts or turnover dynamics in the rental market. The marginal spring effect implies that seasonal variation in pricing is unevenly distributed throughout the year.
Regionally, the Northeast, South, and West all exhibit significantly higher rents than the Midwest, even after controlling for metro-level characteristics. This aligns with historic rental market patterns but may also signal increasing pressure on housing affordability in these regions as climate conditions evolve.

4. Discussion

The results of this study show that the relationship between rising temperatures and rental rates is not uniform across the United States. While earlier models revealed a broadly positive association, the inclusion of increasingly granular fixed effects—especially at the metropolitan level—uncovered significant spatial and temporal variation. These findings challenge the assumption of a universal temperature–rent relationship and point to the importance of local context. Indicating the importance of considering socio-economic factors when developing strategies to adapt to climate change and reduce vulnerability [24].
Model 6, in particular, revealed a negative and statistically significant relationship between temperature and rent in certain metropolitan areas, especially those already experiencing extreme heat. This may reflect diminished livability, tenant migration, or other market responses to environmental stress. Seasonal effects were strongest in the fall and summer, likely due to delayed heat impacts and lease turnover cycles. Regionally, the Northeast, South, and West exhibited significantly higher rents than the Midwest, potentially indicating growing affordability pressures in warmer regions.
These findings suggest that climate-related rent changes are mediated by localized economic conditions, housing supply constraints, and climate adaptation capacity. The following sections explore implications for equity, policy, regional vulnerability, and future research.

4.1. Environmental Injustice and Housing Inequality

The study highlights the disproportionate impact of rising temperatures on marginalized and low-income communities. Research shows that the hottest areas within cities are often located in redlined neighborhoods, which are home to residents already facing significant economic challenges. As these areas lack sufficient green spaces and experience higher temperatures, they are more vulnerable to the compounding effects of environmental injustice and housing inequality. Recent studies have even highlighted a strong correlation between rising temperatures and increased residential water use (RWU). With projections indicating an increasing trend in future RWU compared to the present period due to increased temperatures [25]. Comparable dynamics have been documented globally, where rent pressure and neighborhood change driven by regeneration projects undermined social sustainability [15]. As noted in Section 3.6, Southern metros such as Dallas and Miami exhibited stronger rental responses to heat, while Midwestern cities such as Chicago and Des Moines showed more muted effects—patterns that reflect uneven vulnerability across regions.
Climate change-induced displacement disproportionately affects society’s most vulnerable—those who are poorer, less educated, and least able to economically recover after relocating [26]. The inability of low-income families to relocate to more climate-resilient areas further exacerbates this divide, as wealthier households can migrate to cooler regions or invest in climate mitigation technologies like air conditioning. This situation underscores the need for targeted policies to protect vulnerable populations from climate-related displacement and economic hardship. As climate-related risks intensify, buyer preferences and perceptions are expected to play a greater role in shaping housing demand. Researchers argue that these dynamics will influence “the marketability and valuation of property with varying degrees of environmental exposure and resilience functionality,” reinforcing the economic significance of climate adaptation in real estate markets [27].

4.2. Policy Implications and Climate Resilience

Given the observed regional disparities in temperature sensitivity and the elevated rental burdens found in hotter cities such as Dallas, Miami, and Los Angeles —as highlighted in Section 3.6 and Appendix A. Targeted policy responses may help mitigate the adverse effects of climate change on rental housing markets. Local governments may consider expanding green spaces in densely populated minority neighborhoods and exploring price stabilization measures to ensure revitalization efforts do not inadvertently drive up housing costs. Cooling interventions such as tree-planting initiatives, air-conditioning access programs, or utility-based rate adjustments could potentially reduce renter vulnerability in high-heat environments, while maintaining affordability. To be effective, such efforts would likely need to be paired with tenant protections to avoid the displacement risks that can arise during climate-driven redevelopment, as seen in other urban contexts [15,24].
Additionally, developing a regional thermal baseline (e.g., via cooling degree days) may help policymakers identify where climate-induced housing stress is the greatest. This would enable more targeted resource deployment to communities bearing disproportionate climate burdens in real time.

4.3. Regional Vulnerability to Climate-Induced Rental Increases

Recent data from the Joint Center for Housing Studies underscores the mounting financial toll of climate-related disasters on the U.S. rental housing market. In 2021 alone, 18 major weather events caused over USD 100 billion in damage, placing millions of rental units—especially those affordable to low-income households—at increased risk of loss or disrepair [28]. A key contribution of this research is the identification of regions most susceptible to rising rental prices due to climate change. As highlighted in Appendix A—specifically, Figure A1 and Table A1 for the West, and Figure A3 and Table A3 for the Northeast—both regions were found to be particularly vulnerable, experiencing the highest rental rate increases in response to rising temperatures. This highlights the uneven distribution of climate impacts across different geographic regions, with certain areas bearing a disproportionate share of the burden.
Additionally, the study found that rental rates peak during the fall, with September being the month that exhibits the most significant price increases. This seasonal pattern suggests that climate-induced rental spikes are not only regionally concentrated but also time-specific, further complicating the ability of low-income households to adapt, as the results indicate that when rental rates in some localities are at their highest, the temperatures in these same places are in fact at their warmest.

4.4. Recent Policy Actions on Housing and Heat Resilience

Recent policy efforts aim to address the growing risks posed by extreme heat, particularly in vulnerable communities. A growing body of federal analysis highlights how climate change disproportionately affects already vulnerable populations, reinforcing patterns of inequality in the housing system [29]. This growing exposure threatens to exacerbate the already severe housing affordability crisis, particularly for federally subsidized and low-rent units [28]. The U.S. Department of Housing and Urban Development’s Extreme Heat Resilience Playbook provides guidance on improving housing resilience, with strategies such as increasing energy efficiency, expanding green spaces, and incorporating cooling technologies, especially in low-income areas [30,31]. Currently, the Playbook does not account for the regional variability that has been found in our model. Incorporating regional specific strategies based on temporal variation would strengthen the ability of the Heat Resilience Playbook to provide accurate guidance.
As Ruíz and Mack-Vergara (2023) [20] emphasize, “there is an evident need to develop clear and ambitious initiatives and policies that promote innovative solutions with a vision to achieve fair housing, with a particular focus on those that contribute to or are based on human rights and are resilient and sustainable against climate change” [24].
The White House Summit on Extreme Heat emphasized the importance of federal coordination in tackling extreme heat, bringing together policymakers and community leaders to address public health and infrastructure challenges [32]. Additionally, the National Heat Strategy (2024–2030), developed by NOAA, offers a comprehensive plan to mitigate heat-related risks through measures like green infrastructure, energy-efficient building practices, and public awareness campaigns [33]. These policies align with the patterns observed in this study and may warrant further exploration as potential tools to protect vulnerable populations from the escalating impacts of extreme heat.

4.5. Future Research and Limitations

The housing affordability and climate crises are deeply interconnected challenges [34]. Urban planning policies should incorporate heat mitigation measures as part of broader climate adaptation strategies [35]. It is important to note that while this study identified a positive correlation between rising temperatures and rental rates, causality has yet to be established. Additional research is needed to control for other factors influencing rental markets at the metropolitan level, which may further clarify the underlying mechanisms driving these trends. More research in this area would aid in revealing housing markets most sensitive to rising temperatures, as well as revealing areas that are most resilient. Future research should incorporate alternative climate metrics—such as extreme heat days, cooling degree days, or humidity—to better understand behavioral and cost-related drivers of renter vulnerability. Such findings could aid in the development of mitigation/adaptation strategies.

5. Conclusions

This study demonstrates the significant role that rising temperatures play in shaping the residential rental market, particularly in geographically and economically vulnerable regions. The effects of rising temperatures on rental prices are not uniform across the United States; instead, they are mediated by regional, seasonal, and local economic factors. While higher temperatures often correlate with rent increases, this relationship varies by metropolitan area, seasonal demand, and infrastructure resilience. As the Center for American Progress notes, housing location, design, and demographics directly influence climate vulnerability [34].
Our findings—namely, that rising temperatures affect rental prices unevenly across regions, seasons, and metropolitan areas—have important implications for climate resilience and housing equity. As temperature increases, wealthier renters and homeowners are more likely to adapt, while lower-income households face greater financial strain and displacement risks. This growing divide highlights the need for regionally tailored policy responses.
Policymakers must recognize the spatial and temporal variation in climate-driven rent dynamics and develop mitigation strategies accordingly. Cities should integrate both active and passive cooling technologies to address heat stress across a variety of environments [35]. Potential solutions include thermal-indexed price stabilization, expanded cooling infrastructure, increased investment in affordable housing, and adaptive zoning regulations. Local governments should mandate the use of cool roofs on new buildings, particularly in dense urban areas, and encourage the adoption of shading structures in public spaces [35]. Green urban planning and targeted heat mitigation measures will also be essential, particularly in dense, high-cost cities facing acute climate risk.
Ultimately, this study underscores the deepening intersection between climate change, housing affordability, and social inequality. Without timely intervention, climate-induced rental disparities will worsen. Future research should integrate migration trends, household adaptation behaviors, and energy burdens to refine understanding of climate-market interactions. Addressing these challenges now is vital to ensuring a more equitable and climate-resilient urban future.

Author Contributions

Writing—original draft, M.A.G. and T.G.R.; Writing—review & editing, M.A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

All figures and tables below are based on data from NOAA Climate at a Glance and the Zillow Observed Rent Index (ZORI), 2014–2021.
Figure A1. Sample of Western U.S. metropolitan rental rates and temperature correlations.
Figure A1. Sample of Western U.S. metropolitan rental rates and temperature correlations.
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Table A1. Sample of Western U.S. metropolitan rental rates and temperatures.
Table A1. Sample of Western U.S. metropolitan rental rates and temperatures.
LocationRegionRent PriceMinMaxTemperatureMinMax
Honolulu, HI, USAWestUSD 2191.75
(121.0793)
1894249979 °
(3.640402)
71.785.6
Albuquerque, NM, USAWestUSD 1026.24
(119.9338)
895137059 °
(15.0893)
35.681.3
Phoenix, AZ, USAWestUSD 1238.71
(233.3472)
947186476 °
(14.27897)
53.999.1
Los Angeles, CA, USAWestUSD 2252.02
(236.1321)
1797276965 °
(5.414987)
5475.1
Figure A2. Sample of Southern U.S. metropolitan rental rates and temperature correlations.
Figure A2. Sample of Southern U.S. metropolitan rental rates and temperature correlations.
Sustainability 17 07459 g0a2
Table A2. Sample of Southern U.S. metropolitan rental rates and temperatures.
Table A2. Sample of Southern U.S. metropolitan rental rates and temperatures.
LocationRegionRent PriceMinMaxTemperatureMinMax
Dallas, TX, USASouthUSD 1331.62
(140.6647)
1097170468 °
(14.27536)
42.990.9
Washington, DC, USASouthUSD 1894.02
(86.28779)
1737211960 °
(15.81751)
28.883.9
Miami, FL, USASouthUSD 1856.77
(208.6326)
1562259779 °
(5.383554)
67.485.9
New Orleans, LA, USASouthUSD 1165.84
(68.46238)
1071143572 °
(10.98835)
47.587.1
Figure A3. Sample of Northeast U.S. metropolitan rental rates and temperature correlations.
Figure A3. Sample of Northeast U.S. metropolitan rental rates and temperature correlations.
Sustainability 17 07459 g0a3
Table A3. Sample of Northeast U.S. metropolitan rental rates and temperatures.
Table A3. Sample of Northeast U.S. metropolitan rental rates and temperatures.
LocationRegionRent PriceMinMaxTemperatureMinMax
Philadelphia, PA, USANortheastUSD 1453.30
(105.9592)
1276173257 °
(16.33101)
25.181.9
Syracuse, NY, USANortheastUSD 1051.46
(93.92213)
892133650 °
(17.8691)
10.777.1
Providence, RI, USANortheastUSD 1357.30
(169.9879)
1085177953 °
(16.13284)
18.577.6
Boston, MA, USANortheastUSD 2379.99
(154.9673)
2097270152 °
(16.01944)
19.177
Figure A4. Sample of Midwest U.S. metropolitan rental rates and temperature correlations.
Figure A4. Sample of Midwest U.S. metropolitan rental rates and temperature correlations.
Sustainability 17 07459 g0a4
Table A4. Sample of Midwest U.S. metropolitan rental rates and temperatures.
Table A4. Sample of Midwest U.S. metropolitan rental rates and temperatures.
LocationRegionRent PriceMinMaxTemperatureMinMax
Chicago, IL, USAMidwestUSD 1571.35
(84.98505)
1403177051 °
(18.58981)
15.678.3
St. Louis, MS, USAMidwestUSD 1007.03
(76.83457)
878120258 °
(17.64548)
25.983.3
Des Moines, IA, USAMidwestUSD 1054.63
(48.90772)
941117151 °
(18.02413)
15.478.4
Omaha, NE, USAMidwestUSD 968.82
(86.69487)
805116353 °
(19.51234)
16.580.1

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Figure 1. Methodological framework for analyzing the impact of rising temperatures on residential rental rates.
Figure 1. Methodological framework for analyzing the impact of rising temperatures on residential rental rates.
Sustainability 17 07459 g001
Table 1. Ordinary least squares (OLS) and fixed-effects regressions results.
Table 1. Ordinary least squares (OLS) and fixed-effects regressions results.
Effect of Climate Change on Residential Rental Rates
123456
Average Monthly
Temperature
2.223 ***
(0.270)
4.543 ***
(0.400)
5.912 ***
(0.455)
6.054 ***
(0.546)
5.779 ***
(0.577)
−0.735 ***
(0.157)
Season
Spring-−89.471
(14.404)
−62.150
(61.613)
−60.351
(56.067)
−1.123
(44.344)
21.129 *
(10.212)
Summer-−151.866 ***
(19.012)
−86.321
(64.118)
−85.244
(59.865)
−60.312
(45.560)
198.501 ***
(11.847)
Fall-−70.184 ***
(15.061)
249.165 ***
(64.040)
252.188 ***
(59.602)
39.768
(45.397)
297.311 ***
(11.804)
Region
Northeast---280.768 ***
(12.923)
−51.139
(35.125)
179.805 ***
(8.976)
South---65.081 ***
(12.896)
−441.977 ***
(36.471)
1003.183 ***
(9.835)
West---417.923 ***
(13.396)
626.782 ***
(41.445)
1341.257 ***
(10.355)
Dummies *
Year/Month--XXXX
State----X-
Metro-----X
Adjusted r20.0090.0170.1020.2590.6310.976
Number of obs762476247624762476247624
Constant1156.518
(16.848)
1094.123
(19.184)
893.183
(47. 099)
726.759
(43.633)
1046.835
(39.781)
692.911
(10.559)
Standard Deviation in Parentheses
* p < 0.10, *** p < 0.01.
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Garvey, M.A.; Reames, T.G. A Preliminary Analysis of the Relationships Between Rising Temperatures and Residential Rental Rates in the USA. Sustainability 2025, 17, 7459. https://doi.org/10.3390/su17167459

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Garvey MA, Reames TG. A Preliminary Analysis of the Relationships Between Rising Temperatures and Residential Rental Rates in the USA. Sustainability. 2025; 17(16):7459. https://doi.org/10.3390/su17167459

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Garvey, Michael A., and Tony G. Reames. 2025. "A Preliminary Analysis of the Relationships Between Rising Temperatures and Residential Rental Rates in the USA" Sustainability 17, no. 16: 7459. https://doi.org/10.3390/su17167459

APA Style

Garvey, M. A., & Reames, T. G. (2025). A Preliminary Analysis of the Relationships Between Rising Temperatures and Residential Rental Rates in the USA. Sustainability, 17(16), 7459. https://doi.org/10.3390/su17167459

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