// -----------------------------------------------------------------------------
// Rent-Ka — Rental listings aggregator (Canada, outside Québec)
// Author: Simon-Pierre Boucher — contact@spboucher.ai
// pages/JusteValeur.tsx: "How is fair value calculated?"
//   Transparency page on the estimation framework (rentka/fairvalue.py).
// -----------------------------------------------------------------------------
import { Link } from "react-router-dom";
import FairValueBadge from "../components/FairValueBadge";

export default function JusteValeurPage() {
  return (
    <div className="container legal">
      <span className="kicker">Rent-Ka price analysis</span>
      <h1>How is fair value calculated?</h1>

      <section>
        <p>
          On every listing, Rent-Ka shows an <b>estimated fair value</b> of the
          rent and a badge that places the asking price against the market:
        </p>
        <p className="fv-demo">
          <FairValueBadge verdict="sous" deviation={-0.12} />{" "}
          <FairValueBadge verdict="marche" deviation={0.02} />{" "}
          <FairValueBadge verdict="sur" deviation={0.15} />
        </p>
      </section>

      <section>
        <h3>The method, simply</h3>
        <ul>
          <li>
            <b>1. The market as reference.</b> We start from all the listings
            currently published on Rent-Ka (continuously refreshed by our
            syncs). For each market segment — city, neighbourhood, bedroom
            count — we compute the median rent and its typical range. Rooms and
            shared units form their own segment.
          </li>
          <li>
            <b>2. Features matter.</b> Heat or electricity included, parking,
            furnished, air conditioning, area…: the effect of each feature on
            the rent is measured from the listings themselves, then applied to
            the estimate.
          </li>
          <li>
            <b>3. Comparable neighbours.</b> When the listing is geolocated, we
            refine with the closest comparable listings (same size class,
            within 3 km), giving more weight to the closest and most similar.
          </li>
          <li>
            <b>4. Guardrails.</b> The estimate stays bounded to a plausible
            corridor around the segment reference. Every estimate carries a
            range and a confidence level. If there are not enough reliable
            comparables, or if the advertised price is ambiguous ("from",
            weekly price), <b>no badge is shown</b> rather than a misleading
            verdict.
          </li>
        </ul>
      </section>

      <section>
        <h3>The classification</h3>
        <p>
          The gap between the asking price and the fair value determines the
          badge: <b>below market</b> from −8 %, <b>above market</b> from +8 %,
          <b> at market</b> in between. These thresholds are recalibrated
          periodically by observing the market — notably how quickly
          "below market" listings find takers.
        </p>
        <p>
          Tip: on the home page, the <b>"▼ Below market"</b> filter shows only
          the good deals, best ones first.
        </p>
      </section>

      <section>
        <h3>What it is not</h3>
        <p>
          The Rent-Ka fair value is an <b>indicative statistical estimate</b>,
          recomputed at every sync. It is neither a professional appraisal nor
          a legal opinion on the rent. No data is invented: when the local
          market does not provide enough comparable listings, we say so instead
          of estimating blindly.
        </p>
      </section>

      <div className="fine">
        Questions or observations about an estimate?{" "}
        <Link to="/contact">Write to us</Link>.
      </div>
    </div>
  );
}