In order to promote the balanced development of basic education in China, a new type of equalization measure based on “opportunity equalization” has emerged in recent years, but its policy effect has not been fully studied. Taking the “multi-school zoning” policy implemented in Xicheng District of Beijing in 2020 as an example, this paper theoretically derives and empirically estimates the policy effect of such equalization measures in basic education from the perspective of the capitalization effect reflecting market recognition. The theoretical derivation shows that, by introducing an uncertainty in allocating public services and relaxing the strong binding relationship between the real estate and public education, the “multi-school zoning” policy reduces the overall housing price level and lowers the premium of the real estate around high-quality schools. However, the market’s preference for high-quality elementary schools may shift to a preference for “high-mean, high-certainty” school districts. Based on the data of 107,690 real estate transactions in Xicheng District of Beijing and its four neighboring districts from May 2018 to December 2020, this paper uses DID and Boundary Fixed Effect methods to test the theoretical hypotheses one by one. The results show that, the policy reduces the average housing price in Xicheng District by 1.36%, which has a “peak-shaving” effect on the capitalization premium of education in a school district, lowers the cost of enjoying high-quality public education, and promotes the equalization of basic education to a certain extent. However, the policy also makes the mean and difference in the reputation of elementary schools in school districts replace the reputation of individual elementary schools and become an important factor in the purchase decision. In addition, the policy also leads to significant fluctuations in the transaction volume of the real estate market.

This paper has the following contributions: First, it expands the theoretical study of the capitalization effect of public services, and proves that measures to improve the fairness of opportunity by introducing randomness can produce the equalization effect of public services. Second, it examines a new type of equalization measure based on “opportunity equalization”, which provides new evidence for evaluating the effectiveness of China’s education equalization policy. Third, it provides policy enlightenment for the implementation of local public service equalization, and suggests that in the long run, it is necessary to give full play to the “value capture” function of relevant fiscal tools, which provides stable and sustainable revenue sources for local governments, and forms a virtuous circle between public service provision and local taxation.