This research seeks to demonstrate how machine learning, a branch of artificial intelligence, is able to deliver more accurate pricing predictions, using the real estate market as an example. Utilizing 24,936 housing transaction records, this paper employs Extra Trees (ET), k–Nearest Neighbors (KNN), and Random Forest (RF) to predict property prices and then compares their results with those of a hedonic price model. In particular, this paper uses a feature (property age x square footage) instead of property age in order to isolate the effect of land depreciation on property prices. Our results suggest that these three algorithms markedly outperform the traditional statistical techniques in terms of explanatory power and error minimization. Machine learning is expected to play an increasing role in shaping our future. However, it may raise questions about the privacy, fairness, and job displacement issues. It is therefore important to pay close attention to the ethical implications of machine learning and ensure that the technology is used responsibly and ethically. Researchers, legislators, and industry players must work together to create appropriate standards and legislation to govern the use of machine learning.

Linear regression is a statistical tool used to model the relation between a set of housing characteristics and real estate prices. It estimates the mean value of the response variable, given levels of the predictor variables. The quantile regression approach complements the least squares by identifying how differently real estate prices respond to a change in one unit of housing characteristic at different quantiles, rather than estimating the constant regression coefficient representing the change in the response variable produced by a one-unit change in the predictor variable associated with that coefficient. It estimates the implicit price for each characteristic across the distribution of prices and allows buyers of higher-priced properties to behave differently from buyers of lower-priced properties, even if they are within one single housing estate. Thus, it provides a better explanation of the real-world phenomenon and offers a more comprehensive picture of the relationship between housing characteristics and prices.

As a unique phenomenon of urbanisation in China, the emergence of urban villages has attracted considerable attention from the academic community. Adopting an institutional approach on land development, this study analyses how the land property rights regime affects village-led land development behaviours and spatial outcomes in urban villages. Using a set of reliable data from Shenzhen, the empirical study shows that, although unequal land rights under the current land property rights regime impose severe institutional constraints to the development of urban villages, they actually play a much more important and diversified role in China’s urbanisation than previously recognised. As the primary developers of urban villages, villagers have adopted various land development strategies in response to the changing market environment and internal economic conditions in the dynamic urbanisation process. These dynamic strategies have contributed to the coexistence of sub-optimal industrial development and high-quality housing development in urban villages.