Interesting project to forecast a real estate's selling price (aka, an indicator of value, that is an accepted measure for valuation purposes). However, it is inherently difficult to render a consistent valuation opinion, given this sector inter-relationship (correlation) with harder to forecast economic and other key variables. However, the extent of a lag effect does permit a shorter-term forecast. An example of a significant macro variable includes interest rates, which can/should be decisive in a buyer's decision to rent or buy. The strength of the job market and stock market (impacting 401K, which can finance a down payment) are other examples. Unfortunately, the latter are all subject to somewhat random short term shocks from events including pandemics, natural disasters and even election results (potentially impacting government policies/subsidies),...
And further, there is an apparent somewhat longer-term macro real estate cycle, which can contribute to forecast error. To quote a source:
Real estate markets perform cyclically. The cycles affect output and the absorption of units, and they influence the prices and rents of existing properties and new construction. Expectations for rent increases and the time to start and continue construction are central to the structure of real estate cycles. When participants under forecast rent increases, serially correlated unexpected excess returns trigger construction even if contractors distinguish between relative and nominal price changes. Prices in real estate markets depend on the behavior of the cycle, which in turn affects production and prices. The findings for multifamily housing in Phoenix and Tucson, for example, suggest that cycles are characterized by upside and downside lengths of three years.
Concomitant with the above there is a longer-term demographic shifts (which continue to evolve due to climate change) occurring with older retired people moving to warmer and popular locations both domestically and even possibly foreign countries.
Nevertheless, for the bold, if you have to assess valuation, I would recommend doing so for only a limited horizon.
General model recommendations, a parsimonious model (few variables), as I would employ an auto-regressive time series models with but a few key available variables that have been historically successful in calling turning points at a lag to a market decline.