Improving forecasts of the level and structure of long-run discount rates in the leasehold property market

Transaction-level data will be utilised to explore the level and structure of long-run discount rates applicable to the residential leasehold property market. While techniques such as panel and hedonic regression have traditionally been applied to housing data, issues such as nonlinearity, multicollinearity and heteroscedasticity, present challenges to the ability of traditional regression-based methodologies to make long-term, accurate forecasts. Therefore, this work will compare these traditional regression techniques with two machine learning techniques – a long short-term memory (LSTM) model, and a gradient episodic memory (GEM) model – that are anticipated to overcome these issues endemic to housing data, and provide more accurate and precise forecasts. LSTM models overcome some of the problems with regression models, namely, nonlinearity, and the level of memory over time. Where regression models lack a categorical memory component, LSTM models provide the ability to learn features from the data, as opposed to directly applying a pre-conceived, prior structure. This results in LSTM models being able to better deal with inter-temporal, yet rarely occurring events. GEM models build on the strengths of LSTM, and allow task-based learning, which enables more precise modelling of the behaviour and recurrence of rarely-occurring events within leasehold transaction data.