Peri-urban areas are transition zones that are being urbanized to fulfil the demand for urban space. To ensure an orderly growth and sustainable development, several planning improvements are implemented through master plans, zonal plans and local area plans. It has been notices that interventions suggested by the development plans generate externalities that in turn influence the value of land. Assessment of such externalities is vital to quantify planning obligations and betterment levy. Academic literature on planning externalities has received little attention, and consequently, there are limited studies on internalization of planning externalities. Residential land use occupies nearly half of the urban land, and it is referred to as the key indicator of the urban land market. A significant volume of land transactions originates from residential land use, thus highlighting the criticality of quantifying residential land values. This article, therefore, intends to develop a model to forecast the impact of externalities due to planning interventions on residential land values with a focus on peri-urban areas.
A cross-sectional analysis approach was adopted, and the land price data were collected from one of the leading property listing portals across Delhi-National Capital Region (NCR). The geographical location of urban amenities was collected from the Geographical Information System (GIS) portal of the planning board, and a near analysis was performed to extract locational attributes
The results presented a land valuation model using a machine learning-based random forest regressor (RFR) that can forecast the impact of planning interventions on residential land values.
The findings of the study will contribute towards financial evaluation of various planning scenarios, assessment of planning obligation and betterment levy to achieve the overall development of the peri-urban areas.
Studies on quantification of planning externalities are limited in the Indian context, and data-based evaluation of planning scenarios is yet to be explored in a scientific manner.
