------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset: Private Rental Listings in Beijing, 2015 and 2018 2. Author Information Principal Investigator Contact Information Name: Yi Wang Institution: University of Minnesota Email: wang8262@umn.edu ORCID: https://orcid.org/0000-0002-1486-5051 3. Date of data collection (single date, range, approximate date): 2015-01-01 to 2018-04-01 4. Geographic location of data collection (where was data collected?): Beijing, China -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication 2. Links to publications that cite or use the data: Wang, Yi, and Edward G. Goetz. 2021. “No Place in the City: The Segregation of Affordable Formal Private Rentals in Beijing.” Housing Policy Debate 0 (0): 1–15. https://doi.org/10.1080/10511482.2020.1858925. 3. Sources for data: Lianjia (http://bj.lianjia.com) and Woaiwojia (https://bj.5i5j.com) 4. Recommended citation for the data: Wang, Yi. (2021). Private Rental Listings in Beijing, 2015 and 2018. Retrieved from the Data Repository for the University of Minnesota, https://hdl.handle.net/11299/219254. --------------------- DATA & FILE OVERVIEW --------------------- 1. File list and relationships between files A. Filename: RSBJ_dataset_final.xlsx Short description: Original dataset of rental listings every Sunday from January to March in 2015 and 2018 scraped from Lianjia and Woaiwojia websites. There are two sheets in Excel spreadheet: "bj2015" and "bj2018" B. Filename: RSBJ_dataset_final_2015.txt Short description: An open file format version (Unicode .txt) of the "bj 2015" spreadsheet (ie. rental listings scraped in 2015) C. Filename: RSBJ_dataset_final_2018.txt Short description: An open file format version (Unicode .txt) of the "bj 2018" spreadsheet (ie. rental listings scraped in 2018) -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: The dataset includes rental listings from Lianjia (http://bj.lianjia.com) and Woaiwojia (https://bj.5i5j.com) in 2015 and 2018. I employed a web-scraping software program to collect rental listings every Sunday from January to March in 2015 and 2018.. 2. Methods for processing the data: I removed items that were duplicates from the original raw collections, items that contained invalid or incomplete information, and items that were not about residential units but storage or commercial spaces. We broke the sample into five groups based on the rental price: Group I corresponds to the affordable rentals, and Groups II to V were created by splitting the remainder of the sample into four equal-size clusters. ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: RSBJ_dataset.final.xlsx -- Data Sheet: bj2015 ----------------------------------------- 1. Number of variables: 14 2. Number of cases: 68732 3. Variable List A. Name: id Description: id for the rental housing B. Name: residence Description: name of the residential complex C. Name: title Description: title of the listing D. Name: area Description: floor area of the rental housing (square meters) E. Name: update_dat Description: date of the update F. Name: rent Description: monthly rent G. Name: rentpersqm Description: monthly rent per square meter H. Name: POINT_X Description: Longitude (Geographic Coordinate System: WGS-84) I. Name: POINT_Y Description: Latitude (Geographic Coordinate System: WGS-84) J. Name: affordable Description: whether the rent for the rental housing would be less than 30% of the average household disposable income for city residents (Group I) 0 = no 1 = yes K. Name: G2 Description: whether the rental housing is in Group II 0 = no 1 = yes L. Name: G3 Description: whether the rental housing is in Group III 0 = no 1 = yes M. Name: G4 Description: whether the rental housing is in Group IV 0 = no 1 = yes N. Name: G5 Description: whether the rental housing is in Group VI 0 = no 1 = yes ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: RSBJ_dataset.final.xlsx -- Data Sheet: bj2018 ----------------------------------------- 1. Number of variables: 18 2. Number of cases: 48923 3. Variable List A. Name: id Description: id for the rental housing B. Name: title Description: title of the listing C. Name: residence Description: name of the residential complex D. Name: house_type Description: # of bedroom(s) and # of living room(s) E. Name: area Description: floor area of the rental housing (square meters) F. Name: orientatio Description: orientation of the housing G. Name: rent Description: monthly rent (yuan, not adjusted for inflation) H. Name: views Description: number of views of the listing I. Name: commercial Description: the name of commercial district where the housing is located J. Name: subway_sta Description: distance to the nearest subway station K. Name: update_dat Description: date of the update L. Name: district Description: the name of district where the housing is located M. Name: rentpersqm Description: monthly rent per square meter N. Name: POINT_X Description: Longitude (Geographic Coordinate System: WGS-84) O. Name: POINT_Y Description: Latitude (Geographic Coordinate System: WGS-84) P. Name: affordable Description: whether the rent for the rental housing would be less than 30% of the average household disposable income for city residents (Group I) 0 = no 1 = yes Q. Name: G2 Description: whether the rental housing is in Group II 0 = no 1 = yes R. Name: G3 Description: whether the rental housing is in Group III 0 = no 1 = yes S. Name: G4 Description: whether the rental housing is in Group IV 0 = no 1 = yes T. Name: G5 Description: whether the rental housing is in Group VI 0 = no 1 = yes