diff --git a/src/solvers/spqr.jl b/src/solvers/spqr.jl
index 2f71dd65..d22dfeda 100644
--- a/src/solvers/spqr.jl
+++ b/src/solvers/spqr.jl
@@ -45,7 +45,7 @@ function _qr!(ordering::Integer, tol::Real, econ::Integer, getCTX::Integer,
         E::Union{Ref{Ptr{Ti}}    , Ptr{Cvoid}} = C_NULL,
         H::Union{Ref{Ptr{CHOLMOD.cholmod_sparse}}        , Ptr{Cvoid}} = C_NULL,
         HPinv::Union{Ref{Ptr{Ti}}, Ptr{Cvoid}} = C_NULL,
-        HTau::Union{Ref{Ptr{CHOLMOD.cholmod_dense}}    , Ptr{Cvoid}} = C_NULL) where {Ti<:CHOLMOD.ITypes, Tv<:CHOLMOD.VTypes}
+        HTau::Union{Ref{Ptr{CHOLMOD.cholmod_dense}}    , Ptr{Cvoid}} = C_NULL) where {Ti<:CHOLMOD.ITypes, Tv<:Union{Float64, ComplexF64}}
 
     ordering ∈ ORDERINGS || error("unknown ordering $ordering")
 
@@ -208,7 +208,7 @@ Column permutation:
 
 [^ACM933]: Foster, L. V., & Davis, T. A. (2013). Algorithm 933: Reliable Calculation of Numerical Rank, Null Space Bases, Pseudoinverse Solutions, and Basic Solutions Using SuitesparseQR. ACM Trans. Math. Softw., 40(1). [doi:10.1145/2513109.2513116](https://doi.org/10.1145/2513109.2513116)
 """
-function LinearAlgebra.qr(A::SparseMatrixCSC{Tv, Ti}; tol=_default_tol(A), ordering=ORDERING_DEFAULT) where {Ti<:CHOLMOD.ITypes, Tv<:CHOLMOD.VTypes}
+function LinearAlgebra.qr(A::SparseMatrixCSC{Tv, Ti}; tol=_default_tol(A), ordering=ORDERING_DEFAULT) where {Ti<:CHOLMOD.ITypes, Tv<:Union{Float64, ComplexF64}}
     R     = Ref{Ptr{CHOLMOD.cholmod_sparse}}()
     E     = Ref{Ptr{Ti}}()
     H     = Ref{Ptr{CHOLMOD.cholmod_sparse}}()
@@ -220,9 +220,9 @@ function LinearAlgebra.qr(A::SparseMatrixCSC{Tv, Ti}; tol=_default_tol(A), order
         C_NULL, C_NULL, C_NULL, C_NULL,
         R, E, H, HPinv, HTau)
 
-    R_ = SparseMatrixCSC{Tv, Ti}(Sparse(R[]))
-    factors = SparseMatrixCSC{Tv, Ti}(Sparse(H[]))
-    τ = vec(Array{Tv}(CHOLMOD.Dense(HTau[])))
+    R_ = SparseMatrixCSC{Tv, Ti}(Sparse{Tv, Ti}(R[]))
+    factors = SparseMatrixCSC{Tv, Ti}(Sparse{Tv, Ti}(H[]))
+    τ = vec(Array{Tv}(CHOLMOD.Dense{Tv}(HTau[])))
     R = SparseMatrixCSC{Tv, Ti}(min(size(A)...),
                                 size(R_, 2),
                                 getcolptr(R_),
@@ -235,10 +235,10 @@ function LinearAlgebra.qr(A::SparseMatrixCSC{Tv, Ti}; tol=_default_tol(A), order
                     ReentrantLock(),
                     Tv[])              # _ldiv_workspace (lazily sized on first solve)
 end
-LinearAlgebra.qr(A::SparseMatrixCSC{Float16}; tol=_default_tol(A)) =
-    QRSparse{Float16}(qr(convert(SparseMatrixCSC{Float32}, A); tol=tol))
-LinearAlgebra.qr(A::SparseMatrixCSC{ComplexF16}; tol=_default_tol(A)) =
-    QRSparse{ComplexF16}(qr(convert(SparseMatrixCSC{ComplexF32}, A); tol=tol))
+LinearAlgebra.qr(A::SparseMatrixCSC{Tv}; tol=_default_tol(A), ordering=ORDERING_DEFAULT) where {Tv<:Union{Float16, Float32}} =
+    QRSparse{Tv}(qr(convert(SparseMatrixCSC{Float64}, A); tol, ordering))
+LinearAlgebra.qr(A::SparseMatrixCSC{Tv}; tol=_default_tol(A), ordering=ORDERING_DEFAULT) where {Tv<:Union{ComplexF16, ComplexF32}} =
+    QRSparse{Tv}(qr(convert(SparseMatrixCSC{ComplexF64}, A); tol, ordering))
 LinearAlgebra.qr(A::Union{SparseMatrixCSC{T},SparseMatrixCSC{Complex{T}}};
    tol=_default_tol(A)) where {T<:AbstractFloat} =
     throw(ArgumentError(string("matrix type ", typeof(A), "not supported. ",
diff --git a/test/spqr.jl b/test/spqr.jl
index f12b68d3..8227457c 100644
--- a/test/spqr.jl
+++ b/test/spqr.jl
@@ -116,6 +116,16 @@ end
     @test eltype(F.Q) == eltype(F.R) == eltyA
 end
 
+@testset "single-precision qr factorization works as expected: $eltyA" for eltyA in (Float32, ComplexF32, Float16, ComplexF16)
+    seed!(4)
+    A = SparseMatrixCSC{eltyA, Int}(eltyA <: Real ? sprandn(m, n, 0.3) : sprandn(ComplexF64, m, n, 0.3))
+    F = qr(A)
+    @test eltype(F.Q) == eltype(F.R) == eltyA
+    @test Matrix(F.Q) * F.R ≈ A[F.prow, F.pcol]
+    x = ones(eltyA, n)
+    @test F \ (A * x) ≈ x
+end
+
 @testset "select ordering overdetermined" begin
      A = sparse([1:n; rand(1:m, nn - n)], [1:n; rand(1:n, nn - n)], randn(nn), m, n)
      b = randn(m)