Academic Background — Gabriel Gyojun Shin, Henley Business School— PhD Candidate of Real Estate Finance

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Research Focus:

I study how applied economics and investment analytics improve decision-making in real assets—linking academic methods to industry-grade tools. Current interests include:

  • Property Investment & Infrastructure: valuation, cash-flow resilience, underwriting review
  • Corporate & Debt Finance: DSCR/covenants, refinancing risk, capital-structure design
  • Digital Infrastructure: data-center location economics, power/thermal constraints, grid coordination
  • AI-assisted Research: document triage, disclosure QA, multi-source synthesis → sheet-ready inputs

AI & Machine Learning

Programme summary
Intensive AI/ML training focused on deployable models and responsible workflows for finance and real-asset analysis.

Core topics
Supervised learning; time-series & regime shifts; NLP for investors; model evaluation & calibration; explainability (SHAP/PD); reproducible notebook→sheet pipelines.

What this adds
Faster, more consistent research pipelines; measurable model quality; explainable outputs suitable for classrooms and investment committees.

Insurance & Flood-Risk Analytics

Insurance design & pricing
Risk-based vs cross-subsidized pricing; deductibles/limits; re/insurance layers, ILWs, cat bonds; parametric triggers (rainfall, flood depth, gauge exceedance) and basis-risk mapping.

Quantification & modeling
Frequency–severity; depth–damage functions; non-stationary climate assumptions; exposure/vulnerability integration to DSCR impacts; lender/portfolio stress scenarios.

Flood warning & operations
Hydro-met feeds (radar, gauges, soil moisture, surge); trigger thresholds with SOPs; dashboards for 7-day outlooks, exposure counts, and expected-loss ranges; after-action learning loops.

Difference-in-Differences & Causal Inference (for Real Assets)

Purpose
Isolate policy or infrastructure effects on asset values, liquidity, or risk—beyond correlation.

Designs I use/teach

  • Canonical DID / Two-way fixed effects: treatment vs control before/after a policy (e.g., floodplain remapping, zoning reform, transport upgrade).
  • Event-study DID: dynamic treatment effects over leads/lags to check parallel trends and timing.
  • Staggered adoption DID: modern estimators (heterogeneous timing/impacts) to avoid TWFE bias.
  • RDD / IV (when applicable): geographic or rule-based thresholds (e.g., elevation cutoffs, program score bands); instrument design for endogeneity.
  • Causal ML for heterogeneity: treatment-effect trees/forests to uncover where effects are strongest (asset class, lease type, elevation band).

Applied examples

  • Flood-zone updates → prices/DSCR: estimate premium changes, capitalization into values, and coverage take-up.
  • Transit or data-center siting → rents/land values: quantify accessibility/power-capacity shocks and spillovers.
  • Building-code/ESG policies → capex & NOI: measure compliance costs vs risk premia.

Validity & diagnostics
Parallel-trend checks; placebo tests; covariate balance; anticipation; treatment re-definition sensitivity; cluster-robust and spatially robust SEs; event-window symmetry.

Outputs
Event-study plots, ATT tables with heterogeneity slices (by exposure/intensity), and Excel-ready summary sheets for committees.

Spatial Economics & Econometrics (Market Structure & Spillovers)

Why spatial?
Real-asset outcomes depend on where activity happens—accessibility, networks, and neighborhood effects matter for prices, liquidity, and resilience.

Toolbox

  • GIS & Accessibility: network travel times, isochrones, gravity-style potentials; power-grid and heat constraints for data centers.
  • Spatial econometrics: SAR/SEM/SDM models for spillovers; spatial panel FE; local indicators of spatial association (LISA).
  • Local heterogeneity: GWR/MGWR for varying coefficients across space; cluster detection for “market micro-regions.”
  • Hedonic & land-use models: disentangle structural vs neighborhood attributes; land-use conversion pressure; zoning stringency proxies.
  • Market structure & competition: agglomeration/dispersion metrics for logistics, data centers, and retail; entry/exit dynamics.

Use cases

  • Infrastructure valuation: quantify how new stations, road links, or grid upgrades shift rent and land-value surfaces.
  • Risk mapping: overlay hazard layers (flood, heat) with tenant mix and covenants to derive portfolio-level exposure metrics.
  • Policy evaluation: spatial DID for boundary-based policies (e.g., near-boundary vs far-boundary comparisons).

Deliverables
Map-first narratives with uncertainty bands, spatial spillover coefficients, and Excel summaries of marginal effects at representative locations.

Methods & Data (Academic → Industry)

  • Modeling approach: transparent Excel models for DCF/DSCR/scenario testing; versioned files and change logs.
  • Data sources: S&P Global Market Intelligence (corporate fundamentals, capital structure, pricing), public filings and statistical yearbooks, hydrology & spatial datasets (elevation, network travel times, grid capacity).
  • AI workflows: LLM-assisted parsing/tagging/synthesis with human-in-the-loop validation.
  • Measurement & impact: uncertainty ranges, sensitivity tables, and explicit limitations in each note/model.

Teaching & Collaboration

  • For academia: reproducible notes, compact datasets, and classroom-ready spreadsheets (valuation, DSCR, drawdown/risk, DID/event-study templates, spatial panels).
  • For industry: underwriting checklists, flood-readiness playbooks, spatial exposure dashboards, and digital-infrastructure primers.
  • Collaboration: open to joint research on AI-assisted analysis, flood-risk pricing, infrastructure investment, and spatial market dynamics.

Affiliation & Profile

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