The topic of house price index modelling is one which is central to a significant number of market stakeholders; governments, central banks, homeowners and businesses, among others. The impact which property price indices have on inflation, economic growth and policy-making are profound, yet the methodology and processes behind the generation of these statistics tools are rather opaque. National statistical agencies will typically use one of two de-facto standard methods for modelling the housing market, those being hedonic regression and repeatsales. While these methods bring with them distinct advantages, they also suffer from significant drawbacks. One of the most problematic of the these is the volume of rich property data required by the model. These data requirements often necessitate the use of non-public data sources, usually acquired through privileges as a government agency. As such, it is difficult for end-users of these statistics to verify the veracity, reliability and accuracy of the results. Furthermore, these intensive data requirements induce a typical lag to publication in excess of two months. As a result, not only homeowners and businesses, but even policy-makers are operating on stale information, which is a substantial limitation given the critical influence exerted by the housing market on so many facets of the economy. Our proposal is a novel, geospatially stratified house price index model which can be computed automatically on publicly available datasets. The algorithm does not require additional, privately-held attribute data for each property, nor does it necessitate a great deal of statistical expertise to implement, maintain and interpret, as the existing standards do. In this thesis, we will outline our methodology and demonstrate the performance of the index, initially on the Irish property market. Following an initial study on Irish sale transactions, the model is extended to a database of asking prices for homes online, thus demonstrating the flexibility of the approach. This illustrates the accessibility of the model to operate on a variety of data sources. Finally, our algorithm will be employed to create a property price index for the United Kingdom, where the public dataset of sale transactions is significantly more plentiful. The results of this demonstrate that our index is not only as good as the official hedonic regression model produced by the ONS in the UK, but far exceeds the smoothness and noise reduction achieved by said model, while maintaining a month-to-month correlation in excess of 85%. Moreover, our proposal achieves this with a lag time from data publication in the order of hours, rather than weeks, as per the ONS house price index.