Seismic risk assessments for real estate portfolios: Impact of engineering investigation on quality of seismic risk studies

Abstract

Seismic risk evaluation studies for real estate portfolios conducted by technical professionals (often Civil and Structural Engineers) have become increasingly desirable and common in financial decisions. In this article, we develop a series of risk measures and ratings based on common outcomes from probabilistic portfolio seismic risk assessments. We first define two portfolio risk metrics: Portfolio Expected Loss (PELα) and Portfolio Upper Loss (PULα), where “α” is the annual exceedance probability, or the corresponding return period (“1/α”). PULα/PELα ratio characterizes the uncertainty in estimated portfolio risks which results from the uncertainty in seismic performance of the individual assets. Three uncertainty levels are defined, namely, low, medium, and high, based on the PULα/PELα. We then develop an asset risk metric, called Tail Contribution Index (TCIα), that characterizes the contribution of individual assets to the portfolio losses that fall within the high-consequence “tail” of the portfolio loss distribution. To describe the overall engineering efforts of a portfolio seismic risk study, we develop a portfolio risk metric, called Portfolio Level of Investigation (PLIα), that characterizes the effective level of engineering investigation. Three investigation levels are defined: low ( desktop), medium ( semi-engineered), and high ( engineered), based on the PLIα. Finally, based on the combination of uncertainty level and investigation level, we develop a rating scheme by which the quality (Qα) of a portfolio seismic risk study is characterized. Five quality levels are defined: very poor, poor, fair, good, and very good. These risk indices and ratings can help stakeholders and technical professionals better diagnose and communicate portfolio seismic risks, scope adequate studies, effectively utilize valuable resources, and base financial decisions on risk assessment results that have the desired reliability.

Uses of epistemic uncertainties in the USGS National Seismic Hazard Models

“…However, yet another potential use of such fractile hazard curves is to calibrate a set of earthquake rupture scenarios in the region so that spatial correlations of ground motions can be properly accounted for in regional seismic risk assessments of buildings and critical infrastructure systems (e.g. Lee et al, 2022; Miller and Baker, 2015; Soleimani et al, 2021). Furthermore, by more completely connecting all hazard uncertainties to various risk metrics, scientific efforts can be better prioritized with respect to reducing epistemic uncertainties (Field et al, 2020).…”

Probabilistic risk assessment for reinforced concrete frame structures subject to mainshock‐aftershock sequences

“…investigated the performance of averaged spectral acceleration ( Sa avg ) and induced the Sa avg in structural collapse risk assessment. Similar research has also been conducted to investigate computational framework 12–14 and mathematical model calibration 15,16 . Moreover, the outcomes of these studies were applied to investigate the seismic risk of various kinds of structures 17–20 .…”

“…Similar research has also been conducted to investigate computational framework [12][13][14] and mathematical model calibration. 15,16 Moreover, the outcomes of these studies were applied to investigate the seismic risk of various kinds of structures. [17][18][19][20] The above-mentioned studies serve as a few examples of structural risk analysis under seismic excitations.…”

The financial risk of real estate combined factor analysis with grey prediction in Liaoning Province

“…It was concluded that (1) the spatial correlation effect of China’s real estate financial risk was increasing, and presents the characteristics of multiple superposition and spatial spillover effect; (2) The risk in the eastern region was the highest, the risk in the central region was the middle, and the risk level in the northwest was higher; (3) The financial risk of real estate was formed by the high dependence of local financial resources on land financing. Lee et al [ 16 ] used Portfolio Expected Loss (PEL α) and Portfolio Upper Loss (PUL α) to measure the financial risk of real estate and divide it into five quality levels in order to achieve better risk aversion effect. Larriva and Linneman [ 17 ] completed the real estate financial risk study by using the proportion of mortgage debt to the US GDP.…”