###### Abstract
Therefore, whether a city's current housing market is saturated and whether it still needs to vigorously develop real estate. At present, there is very little research on housing vacancy, especially on the grid scale [19; 20]. From the research data and methods on housing vacancies, [PERSON] [21] used a linear regression method to analyze the HVR and its influencing factors based on census data. [PERSON] and [PERSON] [22] selected the logit models, community gardens (CGs), and existing vacant lots, to simulate the different uses of vacant land and to identify the factors that would help convert vacant land into CGs. [PERSON] [23] proposed a new quantitative method called the vacancy visual analytics method (VVAM) for identifying vacancies in urban populated areas. After defining and analyzing the vacancy phenomenon, [PERSON] [24] also conducted a correlation analysis with various factors, such as infrastructure and traffic conditions, which represented the HVR of the city. [PERSON] [25] selected a city-wide, randomized controlled trial in the United States to demonstrate that structural disrepair and abandonment are key causes of the negative outcomes regarding people's safety through fieldwork. [PERSON] [12] calculated the HVR based on NPP-VIIRS using the ratio of urban light intensity (ULI) and the light intensity of non-vacancy (LINV). These studies of urban vacancies expands from a single housing statistic to combined research methods with multidisciplinary and multidata sources, which can facilitate the study of urban vacancies with a more reasonable and clear understanding. In this study, four scene images of Luojia-1 were used to calculate HVR. The average and minimum values of the calculation results were compared. Then we obtained the HVR closest to the real situation. This method is an extension of the calculation method used by our predecessors and leads to more reliable calculation results. The paucity of available real estate information in China means that many essential data are difficult to obtain. No official data have been released on vacant housing monitoring and evaluation in China whereas academic research on this topic mainly relies on survey data [26], urban nighttime light data [27], and statistics data [28]. The research focuses on the characteristics of urban vacancies at a low resolution. However, there are also certain shortcomings with the existing studies such as a short research cycle. The previous research ignored the dynamic changes in the HVR at different times and different locations [29]. Although some studies have evaluated housing vacancies based on synthesizing monthly and annual data [12; 30; 31], their methods ignored the impact of roads and nonresidential areas using a single data. The final results for calculating HVR are quite different from the real situation and cannot represent the real housing vacancy situation. In this paper, the main urban areas of Xi'an city were used to map the HVR at a fine scale. The built-up areas were extracted by constructing the LJ&POI index. The HVR was calculated after removing the impact of roads and nonresidential areas. Finally, through multiple periods of nighttime light data, the spatial distribution results that could represent the vacancies in Xi'an city for a period of time were obtained. In this way, decision-makers can understand the spatial distribution of housing vacancies in Xi'an city and put forward targeted policy recommendations. At the same time, this calculation method improves the accuracy of HVR calculations, enriches the method for calculating HVRs, and provides a quantitative basis for policy formulation in the real estate industry. The rest of this paper is structured as follows. In Section 2, the study area and data are introduced. Section 3 expounds on the proposed methodology. In Section 4, the results are presented. In Section 5, the discussion is introduced. Finally, the paper is concluded in Section 6.