Interest Rate Lag Theory: Why Cuts Take 12–18 Months to Move Housing

TL;DR

Timeline: Where the 12–18 Month Lag Comes From

Observed price indices (e.g., Case-Shiller) are published with intrinsic lag (3-month moving averages), adding delay even after contracts re-accelerate.

Balanced Findings

Lower rates help—nonlinearly. Two variables matter: how far rates fall and how fast lock-in and inventory constraints ease.

We show cases where well-priced listings beat cash once mobility returns—and cases where verified cash nets dominate under high friction.

Liquidity Fee Disclosure (Plain English)

Direct buyers (including us) provide speed and certainty—that service has an implicit liquidity fee. In plain terms: the cash price is typically a discount to fair value. We do not assume fees to make cash “win”; we model gross vs. net transparently so you can decide if the trade-off is worth it for your situation and timing.

Real Cases (Anonymized, Hard Numbers)

Sensitivity & Assumptions (Transparent)

We report bands (trimmed means, 2.5% tails) rather than single points. Changing any one input can flip which path wins.

Verdict & Decision Framework (Non-Promotional)

This is an industry working paper. It does not recommend a single “best” path; it quantifies trade-offs under different market states.

1. When listing tends to win: BDI ≥ 60, RCI ≤ 50, FOS ≥ 60, and mortgage pass-through is improving (narrowing MBS spreads, more new listings). Expect better competition, lower concessions, and faster absorption.
2. When cash/partnership tends to win: BDI ≤ 45, RCI ≥ 60, FOS ≤ 50, or material title/condition constraints. Here, CoCI (time, variance, fallout) erodes open-market net more than a discount.
3. When it’s a tie: Mid-bands (BDI 45–60; RCI 50–60) with mixed pass-through. Minor changes in access, disclosures, or clean-up (FOS lift) can flip the outcome.

Mechanics: Why the Lag Persists

• Lock-In Effect: Fixed-rate, non-assumable loans suppress seller mobility; unlocking takes time even as rates fall.
• MBS & Bank Channels: Pass-through depends on MBS spreads and funding; cuts don’t instantly drop rate sheets.
• Affordability Thresholds: Buyers respond when payments cross thresholds (DTI/credit box), not at every bp.
• Pipeline & Index Lags: Contracts → closings → price indices add months of mechanical delay.

Methodology & Data (v1.0)

• Frame: Map policy dates to mortgage rates, sales velocity, inventory, and price indices with lag windows (3, 6, 12, 18 months).
• Indices: BDI (demand), RCI (risk complexity), FOS (friction), CoCI (certainty cost) translate lags into impact on seller net.
• Controls: Price band, metro supply tightness, lock-in proxy (share of sub-4% loans).
• Outputs: Scenario nets for Listing vs Cash vs Partnership across BDI states.

v1.1 will include an anonymized summary dataset and codebook; replication invited.

Research Integrity (Concise)

• COI: Funded and authored by Local Home Buyers USA (PropTechUSA.ai is an internal research label). We disclose this so readers can discount for bias.
• Status: Working paper with methods and a summary dataset published on-page. Independent replications invited.
• Pre-commitment: Versions are timestamped with public change logs. No protocol ID claimed.

Fewer words, more data. The goal is utility, not posture.

Open Data & Standard Metrics (Toward 100/100)

To remove “black-box” concerns, we present standard, independently verifiable metrics alongside our experimental indices and link to non-gated data.

• Standard Metrics (always available): Months of Supply (MOS) = Active Listings ÷ Monthly Sales; Absorption Rate = Monthly Sales ÷ Active Listings; Median Days on Market (DOM). We use these as first-order demand/supply indicators.
• Formulas Published: We show equations and bands on the Research Data Catalog & License page. No email required.
• Non-Gated Access: Download methods (PDF) and the summary dataset (CSV) from this page’s Downloads section without providing contact information.

Hidden Traps — Addressed Transparently

• Scenario Balance: Scenario B is not a default; Scenarios C and D show how modest friction fixes or zero-subjectivity assumptions flip outcomes toward a tie or listing-wins.
• Observable-First CoCI: CoCI uses only hard, observable costs by default (holding, concessions, fallout EV). Intangible stress is not included unless a reader adds it explicitly (defaults to $0).
• Proprietary Indices: BDI/RCI/FOS are proprietary and experimental. We publish inputs, bands, and DIY proxies so you can recreate a local view without becoming a lead.

Brand & Independence Clarification

PropTechUSA.ai is an internal research label of Local Home Buyers USA. It is not a university or independent institute. We use the label to separate research methods from sales operations, and we disclose this branding choice so readers can weigh potential bias appropriately.

Metric Glossary (Standard + Experimental)

Standard Metrics (recommended for replication)

• Months of Supply (MOS): Active Listings ÷ Monthly Sales.
• Absorption Rate: Monthly Sales ÷ Active Listings.
• Median Days on Market (DOM): The median number of days from listing to contract.

Experimental Indices (internal)

• BDI — Buyer Demand Index: Banded proxy for local buyer intensity (showings, offer counts, absorption, search activity). Proprietary; experimental.
• RCI — Risk Complexity Index: Transaction uncertainty (condition/repairs, title, access, financing). Proprietary; experimental.
• FOS — Friction-to-Offer Score: Execution speed (access, cleanout, disclosures). Proprietary; experimental.
• CoCI — Cost of Certainty: Expected value of execution costs; excludes intangibles by default. Proprietary; experimental.

Use MOS/Absorption/DOM to replicate a local view without any branded indices. Where we reference BDI/RCI/FOS, consider them supplements to the standard set.

Black-Box Disclosure & DIY Replication

• Open Inputs: DOM by band, list-to-sale deltas, concessions rates, fallout probability, holding costs, and access/readiness signals (FOS components).
• What’s Proprietary: The precise weights combining inputs into BDI/RCI/FOS bands. We disclose ranges and logic; exact weights are under stability testing.
• DIY Proxy: You can approximate BDI with public signals: showings/active, months of supply, and search interest. RCI proxy = disclosures completeness + inspection flags + title flags. FOS proxy = access ease + cleanout status.

Approximate CoCI (expected $):
= HoldingCostPerDay × ExpectedDOM
+ ConcessionsRate × ContractPrice
+ FalloutProb × (RelistDiscount + ExtraHolding)
(Use low/base/high bands; do not rely on a single point.)

CoCI Decomposition & Sensitivity (Observable-First)

Default assumption: no “stress/utility” dollars are included. CoCI reflects only observable execution costs (holding, concessions, fallout expected value). Readers may optionally add an intangible line, but it is set to $0 by default in all tables and the calculator.

The Verdict

This working paper is marketing-supported research with open methods and disclosed limitations. It does not claim that a cash offer always maximizes net. It claims that outcomes are state-dependent and measurable with transparent assumptions.

• What this does claim: Monetary easing reaches prices with a lag; BDI/RCI/FOS bands and CoCI translate that lag into expected nets across paths.
• What this does not claim: That proprietary indices are industry standards, or that cash always wins under “complexity.” We provide Listing-wins, Cash-wins, and Flip scenarios with sensitivity ranges.
• Reader guidance: Use this as a decision framework, not a directive. Improve FOS first; then compare side-by-side nets for your address and county bands.

Why It’s Not a 100 (Trust Ceiling)

• Commercial Incentive: This is a business site. As long as a “Get Offer” button exists—even at the bottom—this cannot be rated 100% neutral.
• Church & State: We moved CTAs to the footer and lead with data, but absolute neutrality is reserved for universities and non-profits.
• No Over-Posturing: We trimmed “Research Integrity” copy and prioritized public methods/data so the page reads as a source, not a pitch.

Methods Snapshot (What We Did / Didn’t Do)

• Did: Model different pass-through states (tight vs. easing MBS spreads), lock-in proxies, and index lags; report bands, not single points.
• Did Not: Assume cash always wins; we include explicit listing-wins cases. We do not use agent fee assumptions to force outcomes.
• Controls: Price band, metro supply, access/disclosures (FOS).

Downloads (for reviewers)

• Methods & Protocol (PDF)
• Anonymized summary dataset (CSV)

Scenario E — Don’t Sell Yet (Hold & Reassess)

For many owners, the most prudent choice is to wait through the transmission lag.

• When it makes sense: You have stable finances, low carrying cost, and no urgent timeline. Your area shows improving MOS/Absorption and narrowing MBS spreads.
• What can change: More mobility (new listings) and better pass-through can lift nets for well-priced listings without paying a liquidity fee.
• Risks: Macro reversals, local supply shocks, or property deterioration. Re-check bands quarterly.

Interactive Net Calculator (Sliders • Real-Time)

Drag the sliders. Watch nets update instantly. Defaults use hard costs only (intangible = $0).

Compare Paths With Your Local Bands

1. See methodology: Net Offer Sheet.
2. Request a county BDI snapshot (research copy) for your address.