'Underwater' Real Estate: Exploring housing market dynamics under severe flooding in Rotterdam

Abstract

Climate change will lead to more extreme climate and weather phenomena, and this includes the increased risk and severity of flooding. Flooding is one of the most destructive and common natural hazards globally, and rising sea levels and extreme precipitation mean that many human settlements will be situated in climate-sensitive areas.

However, the increased threat of floods in coastal cities is not currently sufficiently represented in the housing markets. Contemporary property prices are shaped by the locational advantages of coastal cities, and have a tendency to underestimate or ignore flood risk, even after experiencing a flood themselves. This increases the possibility of structural shifts in housing markets when the flood risk is realised by the public, leading to a depreciation of real-estate property values, and may cause cascading effects in other aspects of the economy, such as the collapse of the regional housing market and reduced economic attractiveness to the region. The Netherlands, for example, is arguably the best-protected delta in the world from flooding. Still, it is vulnerable to the "safe development paradox", where public flood protection measures motivate the continued investment and expansion of flood-risky areas. While Dutch safety standards are very high, they cannot guarantee absolute protection from floods; the country is still at risk from rare-but-severe flooding, or the occasional minor flood, both of which can influence the Dutch housing markets.

The deep uncertainty of future flood risk means that empirical research alone cannot sufficently describe possible responses to housing market shocks from flooding. Therefore, modelling and simulation is useful in testing potential variants of a housing market system, such as testing different dynamics of housing market actors and potential policy levers in various flood scenarios. This is further supported by an increase in publicly-available rich datasets, that allow for spatial and empirical representation of the housing market and climate-induced flood scenarios.


oindent In this thesis, the city of Rotterdam is used as a case study, to explore the usage of empirical data and to model a housing market shocked by various plausible flood scenarios. This is done in two steps: firstly via a data exploration effort in publically-available datasets for the Netherlands, and then consolidating the data into a stylised agent-based model, simulating the transactions of the housing market while several districts experience flooding shocks from different flood scenarios.

Firstly, I have conducted the data exploration aspect using Dutch open data for flood scenarios, housing, local demographics, and empirically-estimated flood discounts. The data was judged on the suitability of the datasets to be incorporated into an empirical model, and on the presence of data gaps while linking between different data. The exploration highlights the potential of modelling the housing market using empirical demographic data based on income level, but only missing certain data to characterise how homebuyers would acquire mortgage financing. Additionally, on the flood damages side, I show the possibility of characterising flood discounts as a function of flood depth, and the depth-damage relation for the Netherlands.

In the modelling part of the thesis study, I have consolidated the data into an agent-based model with a stylised set of relationships for the housing market. The model was designed based on the empirical stylised trends regarding housing markets dynamics, like declining flood risk discounts in property prices over time. In short, the model simulates the purchase transactions of homebuyers, who are discouraged from flood-affected properties, thus leading to a growing demand and price premium for flood-safe properties. This model was then tested with a simple series of experiments, for single flooding and multiple flooding scenarios, based on empirically-grounded severe flood scenarios on Rotterdam. While the model results are limited in terms of prediction for policy purposes directly, the modelling process as a whole illustrates the value of exploratory modelling to refine the understanding of a system. Via this exploration, it was highlighted that there is a need to further characterise flood discounting behaviour around the action of housing market actors.