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Since the 2010s, two worldwide trends have reshaped the urban housing system and induced drastic neighborhood change: the financialization of rental housing and the rise of the platform economy. In China, with continued investment from speculative financial institutions, platform companies aggressively acquired rental houses from individual landlords to develop a platform-based housing rental economy. How does this new rental economy affect housing supply, rents and inequality? This research answers this question by taking Chengdu, China as a case study. A mixed method approach of big data analytics, hedonic pricing model, and field investigations were used to unpack: (a) the pattern of platform houses distribution and its indication of spatial strategies of the financialized platform economy to grab land rent; (b) the effect of such new housing rental economy on housing rents; (c) the effect of the degree of financialization on platform houses' rental prices. The results inform the debate on the disrupt effect of platform economy and housing financialization on equitable urban development, particularly the heterogeneity among cities and countries. This paper contributes to understanding financial investors' glocalization strategy and the state's territorialization strategy as two crucial factors for the variegation of rental housing financialization. • Houses with small living room and more bedrooms that can generate more rent. • Platform companies tend to choose houses nearby public transport services. • High concentration of platform houses increases rental price of surrounding normal houses. • Degree of financialization significantly increases the price premiums of platform houses. [ABSTRACT FROM AUTHOR]

Ridesourcing services such as Uber, Lyft, and DiDi are purported to be more efficient than traditional taxis because they can match passengers with drivers more effectively. Previous studies have compared the efficiency of ridesourcing and taxis in several cities. However, gaps still exist regarding the measurement and comparison between the two modes, and the reasons for the higher efficiency of ridesourcing have not been empirically examined. This paper aims to measure, compare, and explain the efficiency and variation of DiDi and taxis. The case study is conducted in Chengdu, China. We use vehicle occupancy rate (VOR) as the efficiency measure - the percentage of time that a vehicle is occupied by a fare-paying passenger. We measure the VORs of DiDi and taxis and their spatial and temporal variations using the trip origin-destination data for DiDi and the trajectory data for taxis. The VOR patterns between DiDi and taxis are compared and contrasted, and the underlying factors that affect the difference are examined: more efficient driver-rider matching algorithm, larger scale of ridesourcing services, and the number of taxi trips per capita. Results show that the overall VOR of DiDi is six percentage points higher than taxis on the weekday and 12 percentage points higher on the weekend. However, the VOR of taxis is slightly higher than DiDi during the weekday morning peak hours and in downtown areas. Regression models reveal that the more efficient matching and the greater scale of DiDi drivers enlarge the VOR gap between DiDi and taxis, while the number of taxi trips per capita reduce the gap. The findings have implications for both business operation and transportation policies in terms of service design, service coordination, and location-specific regulations. • The efficiencies of DiDi and taxi are measured by vehicle occupancy rate (VOR). • A distribution fitting approach was developed to measure the VOR from the OD data. • Comparison shows that DiDi is more efficient than taxi except for in the city center. • The more efficient matching and the greater scale of DiDi enlarges the efficiency gap. • The number of taxi trips significantly reduces the efficiency gap. [ABSTRACT FROM AUTHOR]