Do people consider the warnings given by scientists and governments about the risk of sea level rise when making their investment decisions? We estimate the impact of this information on house prices using data from all real estate transactions in the district with a spread frame embedded in a hedonic price model. Using a case study, we ask a simple question: Do people consider the warnings given by scientists and governments about the risk of sea level rise when making their investment decisions.
They find a very small but statistically significant difference. 2019) focuses on the property value of SLR protection in the Chesapeake Bay in the United States. Public disclosure of risks, even in the absence of an actual event, has been shown to lower prices. For example, a study of flood risks in Auckland, New Zealand, found that properties in the flood plain were discounted by 2.3% when their risk profile became publicly available (Samarasinghe and Sharp 2010).
In the presence of the viewing facilities, coastal risk devalued properties in North Carolina beach communities by approximately 11%. Because both groups are located in the same area, their property values are affected by similar concurrent factors. In the specification, the variable 𝐴𝑓𝑓𝑒𝑐𝑡𝑖 has the value 1 if the property (i) lies within the coastal hazard lines and 0 otherwise.
In the Kapiti Coast district, there have been very few insurance claims related to hydrometeorological hazards over the past 30 years (Fleming et al. 2018).
Study area and Data
The hazard risk information was then placed on affected property's Land Information Memorandums (LIMs) and notification letters were sent to affected property owners on 25 August 2012 as required by the Local Government Official Information and Meeting Act.4 The coastal LIMs contained neighborhood maps of coastline . projections to inform people about the hazard risk in their environment. In December 2013, Judge Joe Williams ruled that under Section 44A(2)(a) of the Local Government Official Information and Meetings Act, KCDC had no choice but to take note of coastal hazard information, contained in the Shand report, not on LIMs.”. 34;The lines were grossly simplistic as a summary of the complex Shand information and have the potential to seriously affect the value and marketability of coastal properties."
Due to pressure from the group Coast Ratepayer United, KCDC decided to remove all coastal erosion line maps and related explanatory text from the LIMs in October 2014 (KCDC 2014). The first objective is to identify title transfers in the coastal district of Kapiti that have occurred within the time frame of the study (2009-2018). This enables us to ascertain the exact land title associated with each of the 8,436 detached house sales transactions that occurred within the Kapiti Coastal District during the study time period.
The polygons of the district's land titles were overlaid with line themes in the character exchange format (DXF) representing each of the four modeled 'future coastlines'. The period in which the hazard maps appeared on LIMs (September 2012 through October 2014) does not seem unusual for the affected properties in terms of the number of sales when plotted against the control group and for the entire period, as shown in Appendix Figure 1. Just as all other properties experienced a slowdown in sales starting in 2006 and bottoming out in 2008; this was the local manifestation of the global financial crisis.
Residential sales volumes did not recover until a few years later, in 2011, although they never reached the peaks of the previous real estate cycle. First, the media reported that many affected property owners subsequently sold their houses after successful litigation and the removal of hazard warnings from LIMs (e.g., Cann 2017). The volume of transactions involving affected properties in the months following the removal of hazardous lines from LIMs in October 2014 is within normal range; any increase is merely correlated with the general increase in real estate sales in the county.
This trend stems from the premium placed on coastal amenities such as beach access and uninterrupted sea views. 7 In the table of descriptive statistics, the group in question includes all residential properties that fall within the four coastal danger lines. In addition, the houses in the treatment groups tend to be on steeper land and have better sea views than the houses in the control groups.
Results
As these properties would be 'first to go' with the highest risk of exposure to flooding and coastal erosion, this is to be expected. It appears that coastal property buyers are more aware of coastal hazard risks, but the effect is still small and imprecisely identified. In the first specification, we use the sales price per square meter as the dependent variable in the regression.
In the second, we estimate the same equation as in Table 2, but use the 'post' period as the period in which the risk warning was attached to the LIM (September 2012 to October 2014 only). In the third iteration (Appendix Table 3), we estimate only the price of land, rather than the total price of the property (which includes both the price of the land and the price of the dwelling). For the long-term horizons of the scenarios we examine (50-100 years), a large part of the property value comes from the value of the land (as the home depreciates and eventually becomes obsolete).
We hypothesize that coastal erosion risks can mostly affect the sale price through changes in land valuation. We divide the land price by deducting, from the sale price, the estimated value of the dwelling (see Appendix A for an explanation of land value estimation). The results for the three alternative specifications, as shown in the appendix, are very similar to the results of the standard regressions.
In none of the alternative specifications are the difference-in-difference effect estimates statistically significant. 8 In this regression set, we apply robust standard errors to control for heterogeneity in the error term. We then examine the effects of coastal hazards on property prices over time by estimating annual regressions for each hazard group.9 The results are shown in Figure 2.
In short, erosion risk information placed in LIM reports appeared to have little effect on property price. We also estimated spatial regression models to control for spatial dependencies in property prices (ie, property prices are affected by property transaction prices in the immediate neighborhood; see Appendix B for specification details). Supporting the above findings, we find that the effect of risk information is insignificant across the different models, specifications, and treatment groups.
Conclusions
The estimated spatial autoregressive (𝜌) and autocorrelation () coefficients are statistically significant; and the sale price of a property is positively influenced by neighboring properties'. Horace advises Kapiti Coast home buyers are 'seizing the day' and largely ignoring the future risks to their properties. On the other hand, this assumes that future buyers will continue to ignore this risk so that later sales will not result in a significant loss, not unlike a scheme that Charles Ponzi would have approved.
Bernstain, Asaf, Matthew Gustafson, Ryan Lewis, 2018, Disaster on the horizon: The price effect of sea level rise. Available at https://www.stuff.co.nz/environment/99165184/onethird-of-kpiti-coast-properties-in-hazard-lines-battle-have-since-been-sold. Public Insurance and Climate Change (Part One): Past trends in weather-related insurance in New Zealand.
Estimating the Economic Cost of Sea Level Rise on Coastal Real Estate in the Tampa Bay Region, Florida. The Effect of Seismic Hazard Information on Property Prices: Evidence from a Spatial Regression Discontinuity Design. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Estimating Recent Local Effects of Sea Level Rise on Current Real Estate Losses: A Case Study of the Miami-Dade, Florida Housing Market. Coastal Adaptation to Climate Variability and Change Investigating Community Risk, Vulnerability and Resilience at the Manaia Settlement, Hauraki-Waikato, Aotearoa-New Zealand. Available at https://www.qv.co.nz/property-insights-blog/how-long-people-are-holding-on-to-houses-for-/65.
The double trade-off between adaptation and mitigation of sea-level rise: the use of FUND. House prices and public disclosure of flood risk: A difference-in-differences analysis in Finland. Estimating the Economic Costs of Greenhouse Gas Sea Level Rise: Methods and Application to Support a National Survey.
There are 4 classifications, corresponding to the 4 coastal hazard lines, as shown in Figure 1, with an example from Waikanae. VC is the appraised value of the property that is then used for property tax assessment and is often used as an indication of the potential price during home sales; it is given separately for the value of the land and the apartment. Therefore, we convert the house price to year (t) using the coefficients of the year of sale (t) and estimated year VC (r) models from our hedonic regression model.
We implement three different spatial regression models including the Spatial Autoregressive (SAR) Model, the Spatial Error Model (SEM) and the Spatial Autocorrelation (SAC) Model. We use spatial panel maximum likelihood estimation for the set of robust standard error regression models as described below. In these models, there are two possible types of interaction effects between units: endogenous spatial interaction effects between the dependent variable (𝑊𝑌𝑖) and spatial interaction effects between error terms (𝑊𝜗𝑖).
When the distance is beyond a predetermined level, we assume no spatial effects.
