Traditional House Price Indices (HPIs) typically rely on median or average property prices within a given timeframe—usually quarterly—to measure price trends. However, this method can lead to distorted results due to the uneven distribution of transactions across various price segments. For instance, a quarter dominated by high-end property sales may inflate the index, while one with predominantly lower-value transactions may deflate it. Such fluctuations often reflect changes in the transaction mix rather than true movements in property prices. In contrast, the Ressex HPI developed by Liases Foras adopts the repeat sales methodology. It tracks price changes for individual projects across their lifecycle, with the cumulative change forming the final price index for each project. Since indices are generated at the pin-code level, the average appreciation or depreciation across all projects within a pin-code is used to determine its overall price movement. Furthermore, Ressex HPI calculations use floating-point values, ensuring precision without rounding to integers—capturing nuanced shifts in pricing that traditional indices may overlook.

Before venturing into RESSEX HPI, Liases Foras has demonstrated extensive experience in the creation of housing price indices, particularly through our significant collaboration with the National Housing Bank (NHB). Our involvement included designing the comprehensive data architecture and digital workflow specifically for the Housing Price Index (HPI) focused on under-construction residential properties. A key undertaking was the complete revamp of the entire RESIDEX system. This ambitious project incorporated advanced automation and sophisticated data analytics, resulting in dynamic, interactive, and downloadable indices that provide valuable insights for buyers, sellers, and lenders alike. Our team at Liases Foras comprises a diverse panel of experts, including urban planners, IT professionals, and data analysts. This multidisciplinary expertise enabled them to develop robust solutions for various aspects of data management, encompassing data collection, extraction, cleaning, process automation, and geo-coding. Furthermore, our commitment to methodological rigor is highlighted by the in-depth studies of international practices and statistical techniques, which were crucial in formulating the precise concept and methodology for HPI computation. Among the notable achievements are the strategic shift of the base year to 2012-13, the expansion of coverage to include all State Capitals and Smart Cities, a detailed revision of residential segmentation, and the crucial harmonization of the index with the Reserve Bank of India's (RBI) HPI. We also spearheaded the introduction of automation, significantly enhancing data accuracy and reducing processing time. Liases Foras adeptly navigated the complexities of developing a granularized, turnkey solution within India's diverse and often unorganized real estate sector. This challenging endeavor involved meticulously collecting primary market data, factoring in various associated charges, and judiciously weighting each data set to ensure comprehensive and accurate representation. Ultimately, they successfully created a fully automated enterprise solution that streamlines data collation, sorting, and index formulation, alongside developing contemporary methods for index computation, which include the application of hedonic regression and a modified Laspeyres method that intelligently incorporates weighted average prices.

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