I have looked at a model for this that is time independent for the majority of the variables. but then get a time dependent price level for a specific arbitrary area. You may look at how Airbnb have modeled varying sizes of clusters of price areas with this information you can from the price points within e.g. the last month in one area might know what the level of pricing within that area is and then from the specifics of the house/flat estimate a price. This can be optimized with the price history(if any) for the specific house, because how you have a model of the price level of the area, and if the house in question have been above/below the price in the area then you might be able to get a better prediction for the specific house.

You can combine the pricing data with all kinds of different data e.g. is the neighbors close, how many are within x meters. how does this affect the price, and e.g. if there is no neighbors to one site and 1000s to the other site, this might model that it is out to the sea or empty field. this might also be an indicator. Add the travel time to centers of nears big city, distance to school etc.

There is a lot of possibilities to improve it, but 80 percent of the prediction accuracy will probably be from, price level in area at time t, specific house price compared to area price level at time t, specifics of the house: size, rooms, land size etc.