For several decades, the United States has been committing to improving air quality by empowering its Environmental Protection Agency (EPA) and has achieved excellent results on a large scale. However, EPA has reported that, there were still a large number of population, over 81 million in 2019, living in counties with air quality concentrations above one or more National Ambient Air Quality standards (NAAQs) in the US. Currently, in some major intensive manufacturing cities, air pollution still presents at relatively high levels, such as Los Angeles, New York, Chicago, Dallas, and Houston. In relevant environmental studies, environmental justice is often highlighted that disadvantaged populations are often observed experiencing higher level of air pollutions, but fewer studies have adopted quantitative methods to assess the effectiveness of environmental policies. Taking the Houston area as an empirical case, this study explores the impact of air pollution on local housing prices and racial distributions, and examines the impact of regional environmental policies on housing prices. As the most populous city in southern US, Houston is famous of its energy industry and the majority working class population own at least one vehicle. Industrial production and traffic emission are the two primary sources of air pollution in the urban area. Additionally, high temperature stimulates the creation of smog and ozone, causing public health concerns to the local communities. Even though that the overall air quality is good in this area, there are still many days in a year when the air pollution levels exceed the limit, especially the ground level ozone. This study employs the Hedonic price model to quantify the impact of urban ozone pollution on local communities and residents, as well as to test the responses of housing prices to environmental policies. The Hedonic price model recognizes the housing prices as a bundle of prices of many different characteristics, often referring as the implicit prices. To evaluate the impact of air pollution, ground level ozone is considered as one of many explanatory variables that affect housing prices. Ozone data is obtained from Texas Commission on Environmental Quality (TCEQ), which is the state environment agency monitoring daily air quality. In geographic information system (GIS), inverse distance weighted (IDW) method is applied to interpolate site observed ozone data to the entire study area, and then each housing unit is assigned with an estimated ozone value by their location. The other explanatory variables in the model include housing structure variables, neighborhood quality variables, and demographic variables. To identify city centers, Census Transportation Planning Products (CTPP) data is obtained to identify major Transportation Analysis Zones (TAZs) that have the highest job densities as city centers. Moreover, EPA has passed three ozone standards, 1997 standard, 2008 standard, and 2015 standard, based on which metropolitan areas are being identified as attainment or nonattainment areas. Four dummy variables are included to represent four periods of time before and after governmental regulations became effective on air quality. There are several interesting and important findings. First, this study finds that housing prices follow the general market trend. Housing prices are negatively correlated with home age, distance to central business districts, and distance to nearest highway, but positively correlated with floor area, school district quality, and median area incomes. Second, accessibility analysis shows that housing prices decrease with the increasing distance from the urban centers, which reflects the special pattern of urban development in the US. Housing units locate close to city centers are generally older, and most of the newer housing units are built in the suburbs. Housing units with high accessibility to city centers and transportation facilities tend to have higher transaction prices as land value is relatively higher within the city than in the suburbs. In addition, this study finds that air pollution has an unbalanced impact on housing prices. The regression results indicate that the ozone levels positively correlate with median housing value. Areas close to city centers tend to experience higher level of ozone. Percentages of Asian population, Hispanic population, and African American population are used to estimate the impact of demographics on housing prices. The regression also shows that minority populations are negatively correlated with housing prices. The evaluation of the environmental standards shows that environmental policies have two major effects. One is that the policies have lagged effects on housing prices. The other one is that those policies, in general, have positively significant impacts on local housing prices. As air pollution standards become more and more strict, air quality could be potentially improved over time. In future research, improvements can be made by incorporating more explanatory variables and by collecting more air monitoring data. Additional site monitoring air quality data could improve the accuracy of interpolation method. This study is among one the few studies exploring the relationship between air pollution and the housing market in the Houston area, and it discusses the effectiveness of local environmental policies in-depth.