Real estate is considered to be an imperfect market. It does not meet the standards of a hypothetical perfectly competitive market. This is partly due to information asymmetry. The Dutch housing market, however, seems to be moving from a market with limited information, to a market with an increasing amount of available information. This reasoning makes housing increasingly attractive for data analytics. Several data solutions applied have been programmed to date. Automated Valuation Models (AVMs) are one example, where data is mainly used to predict the value with vacant possession of owner-occupied housing. Most AVMs apply a hedonic pricing model where "value" is decomposed into housing and locational characteristics. These characteristics from rental and owner-occupied housing transactions and valuations have been collected from databases of Colliers International to construct an AVM. Indexation plays a crucial role in AVMs to correct for the difference in time of historic transactions. This is where a hedonic index for Dutch owner-occupied housing is constructed and tested with 7,500 repeated sales. Compared to the index of the Dutch Central Bureau of Statistics, this index is 32%more accurate in areas with a high urban density and 19%in areas with a low urban density. This is due to the inclusion of housing and locational characteristics. Apart from an AVM to predict the value with vacant possession for owner-occupied housing, a similar methodology can be used to construct an AVM to predict the market rent of rental housing. Market rent can be compared to the actual rent and the difference is an input for a machine learning algorithm that combines both AVMs and predicts the value in use. This algorithm has been trained and tested on anonymised data from valuation models. The accuracy becomes better with the inclusion of Colliers’ valuation experts giving their opinion on future trends of the ratio between value with vacant possession and value in use. This makes the algorithm particularly driven by "man and machine", where the experts rely on knowledge and experience. This combination has led to an algorithm to go from value with vacant possession to value in use (predicting the ratio) with a mean absolute error of 2.1%and a median absolute error of 0.9%, tested on 1,400 observations. The value in use finds its use case especially with investors who want to get a quick and accurate estimate the value of their housing portfolio.