diff --git a/search/ranker.cpp b/search/ranker.cpp
index 3cd82c4d00..d11065638d 100644
--- a/search/ranker.cpp
+++ b/search/ranker.cpp
@@ -433,6 +433,24 @@ class RankerResultMaker
       info.m_matchedFraction =
           totalLength == 0 ? 1.0
                            : static_cast<double>(matchedLength) / static_cast<double>(totalLength);
+
+      auto const isCountryOrCapital = [](FeatureType & ft) {
+        auto static const countryType = classif().GetTypeByPath({"place", "country"});
+        auto static const capitalType = classif().GetTypeByPath({"place", "city", "capital", "2"});
+
+        bool hasType = false;
+        ft.ForEachType([&hasType](uint32_t type) {
+          if (hasType)
+            return;
+          if (type == countryType || type == capitalType)
+            hasType = true;
+        });
+
+        return hasType;
+      };
+      info.m_exactCountryOrCapital = info.m_errorsMade == ErrorsMade(0) && info.m_allTokensUsed &&
+                                     info.m_nameScore == NAME_SCORE_FULL_MATCH &&
+                                     isCountryOrCapital(ft);
     }
 
     CategoriesInfo const categoriesInfo(feature::TypesHolder(ft),
diff --git a/search/ranking_info.cpp b/search/ranking_info.cpp
index d740241e06..d41502aee8 100644
--- a/search/ranking_info.cpp
+++ b/search/ranking_info.cpp
@@ -33,6 +33,7 @@ double constexpr kFalseCats = -0.4172461;
 double constexpr kErrorsMade = -0.0391331;
 double constexpr kMatchedFraction = 0.1876736;
 double constexpr kAllTokensUsed = 0.0478513;
+double constexpr kExactCountryOrCapital = 0.1247733;
 double constexpr kNameScore[NameScore::NAME_SCORE_COUNT] = {
   0.0085962 /* Zero */,
   -0.0099698 /* Substring */,
@@ -57,6 +58,7 @@ static_assert(kDistanceToPivot <= 0, "");
 static_assert(kRank >= 0, "");
 static_assert(kPopularity >= 0, "");
 static_assert(kErrorsMade <= 0, "");
+static_assert(kExactCountryOrCapital >= 0, "");
 
 double TransformDistance(double distance)
 {
@@ -119,6 +121,7 @@ void RankingInfo::PrintCSVHeader(ostream & os)
      << ",PureCats"
      << ",FalseCats"
      << ",AllTokensUsed"
+     << ",ExactCountryOrCapital"
      << ",IsCategorialRequest"
      << ",HasName";
 }
@@ -142,6 +145,7 @@ string DebugPrint(RankingInfo const & info)
   os << ", m_pureCats:" << info.m_pureCats;
   os << ", m_falseCats:" << info.m_falseCats;
   os << ", m_allTokensUsed:" << info.m_allTokensUsed;
+  os << ", m_exactCountryOrCapital:" << info.m_exactCountryOrCapital;
   os << ", m_categorialRequest:" << info.m_categorialRequest;
   os << ", m_hasName:" << info.m_hasName;
   os << "]";
@@ -162,6 +166,7 @@ void RankingInfo::ToCSV(ostream & os) const
   os << m_pureCats << ",";
   os << m_falseCats << ",";
   os << (m_allTokensUsed ? 1 : 0) << ",";
+  os << (m_exactCountryOrCapital ? 1 : 0) << ",";
   os << (m_categorialRequest ? 1 : 0) << ",";
   os << (m_hasName ? 1 : 0);
 }
@@ -202,6 +207,7 @@ double RankingInfo::GetLinearModelRank() const
     result += kErrorsMade * GetErrorsMadePerToken();
     result += kMatchedFraction * m_matchedFraction;
     result += (m_allTokensUsed ? 1 : 0) * kAllTokensUsed;
+    result += (m_exactCountryOrCapital ? 1 : 0) * kExactCountryOrCapital;
   }
   else
   {
diff --git a/search/ranking_info.hpp b/search/ranking_info.hpp
index 59c0132bfe..c3afa637e3 100644
--- a/search/ranking_info.hpp
+++ b/search/ranking_info.hpp
@@ -65,6 +65,10 @@ struct RankingInfo
   // True iff all tokens retrieved from search index were matched without misprints.
   bool m_exactMatch = true;
 
+  // True iff feature has country or capital type and matches request: full match with all tokens
+  // used and without misprints.
+  bool m_exactCountryOrCapital = true;
+
   // Search type for the feature.
   Model::Type m_type = Model::TYPE_COUNT;
 
diff --git a/search/search_quality/scoring_model.py b/search/search_quality/scoring_model.py
index 2d62aa18f8..f74242fb7a 100755
--- a/search/search_quality/scoring_model.py
+++ b/search/search_quality/scoring_model.py
@@ -21,7 +21,7 @@ RELEVANCES = {'Harmful': -3, 'Irrelevant': 0, 'Relevant': 1, 'Vital': 3}
 NAME_SCORES = ['Zero', 'Substring', 'Prefix', 'Full Match']
 SEARCH_TYPES = ['POI', 'Building', 'Street', 'Unclassified', 'Village', 'City', 'State', 'Country']
 FEATURES = ['DistanceToPivot', 'Rank', 'Popularity', 'Rating', 'FalseCats', 'ErrorsMade', 'MatchedFraction',
-            'AllTokensUsed'] + NAME_SCORES + SEARCH_TYPES
+            'AllTokensUsed', 'ExactCountryOrCapital'] + NAME_SCORES + SEARCH_TYPES
 
 BOOTSTRAP_ITERATIONS = 10000